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The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
i
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
ii
This document contains North Carolina's attainment demonstration for the Hickory and
Greensboro/Winston-Salem/High Point fine particulate matter nonattainment areas, which
demonstrates that both of these areas will meet the National Ambient Air Quality Standards for
fine particulate matter by April 5, 2010. These areas include the entire counties of Catawba,
Davidson, and Guilford.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
iii
INTRODUCTION
Fine particulate matter, also known as fine particles and PM2.5, refers to airborne particles less
than or equal to 2.5 micrometers (μm) in diameter. Fine particles are treated as though they are a
single pollutant, but they come from many different sources and are composed of many different
compounds. PM2.5 exposure adversely affects human health, especially respiratory and
cardiovascular systems. Individuals particularly sensitive to PM2.5 exposure include children,
people with heart and lung disease, and older adults.
A variety of meteorological and geographic factors influence the concentration levels of fine
particles, including both the regional and local distribution of urbanized areas, primary and
precursor emissions sources, and natural features such as oceans and forests. PM2.5
concentrations can also be high and exceed the national ambient air quality standards (NAAQSs)
for fine particulate matter at any time of the year. Therefore, the United States Environmental
Protection Agency (USEPA) mandates the year round monitoring of PM2.5 concentrations
throughout the country (40 CFR 58.App. D, 4.7).
NATIONAL AMBIENT AIR QUALITY STANDARD
In 1997, the USEPA promulgated the primary (health) and secondary (welfare) NAAQSs for
PM2.5 (40 CFR 50.7), setting the standard at a 15.0 micrograms per cubic meter (μg/m3) annual
average and at a 65 μg/m3 daily or 24-hour average. A violation of the annual PM2.5 NAAQS
occurs when the annual average PM2.5 concentration averaged over a three consecutive year
period is equal to or greater than 15.1 μg/m3. A violation of the daily PM2.5 NAAQS occurs
when the annual 98th percentile of daily PM2.5 concentration averaged over a three consecutive
year period is equal to or greater than 66 μg/m3. The annual or daily PM2.5 design value for a
nonattainment area is the highest design value for any monitor in that area.
The USEPA designated areas as nonattainment for the annual and daily PM2.5 NAAQSs based
upon air quality monitoring data measured during 2001, 2002 and 2003. The effective date of
nonattainment designations was April 5, 2005.
NATURE OF PROBLEM IN NORTH CAROLINA
In North Carolina, there were two areas designated as nonattainment for violating the annual
PM2.5 standard (Figure 1). All areas of North Carolina met the daily PM2.5 standard. This PM2.5
attainment demonstration submittal covers the Hickory PM2.5 nonattainment area (Catawba
County) and Greensboro/Winston-Salem/High Point PM2.5 nonattainment area (referred to as the
Triad area and consists of Davidson and Guilford Counties) with respect to the violations of the
annual PM2.5 standard.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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When the annual PM2.5 concentrations in both nonattainment areas are analyzed by the
percentages of their individual component species, the organic carbon (OC) and sulfate (SO4)
components each account for approximately one-third of the total PM2.5 mass, the ammonium
component makes up approximately ten percent of the total PM2.5 mass, and the remaining
nitrate (NO3), elemental carbon, crustal material, and particle bound water components each
contribute approximately five percent or less of the total PM2.5 mass. The percentages of species
contribution fluctuate throughout the year with the most significant changes to SO4 and NO3.
SO4 is more pronounced in the summertime or warm season months than during the wintertime.
NO3 fluctuates from almost undetectable in the summertime to as much as ten percent
contribution of the total PM2.5 mass during the coldest portion of the winter.
The speciated analysis of the PM2.5 concentrations in the Hickory and Triad PM2.5 nonattainment
areas demonstrates that the OC and SO4 components are the most important portions of the total
PM2.5 mass throughout the year. OC is predominately attributed to biogenic emissions sources.
SO4 is associated with sulfur dioxide (SO2) emissions. When evaluated across North Carolina
and also throughout both nonattainment areas and surrounding regions, the SO2 is primarily from
the point source sector. For this reason, SO2 emissions controls from point sources are believed
to be the most appropriate strategy for addressing the PM2.5 nonattainment issues for Hickory
and the Triad.
CONTROLS APPLIED
Several control measures already in place or being implemented over the next few years will
reduce stationary point, highway mobile, and non-road mobile sources emissions. The expected
Federal and State control measures were modeled for the attainment year of 2009.
The Federal control measures that were modeled included the Tier 2 vehicle standards; the
heavy-duty gasoline and diesel highway vehicle standards; low sulfur gasoline and diesel fuels,
large non-road diesel engines standards; the non-road spark-ignition engines and recreational
engines standard; and the Clean Air Interstate Rule (CAIR). Due to the Court challenges of
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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CAIR in 2008, the USEPA will be making changes to this program soon. However, the existing
CAIR rules will remain in place until the USEPA promulgates changes to the program.
The State control measures that were modeled included the Clean Air Bill, in which the vehicle
emissions inspection and maintenance program was expanded from 9 counties to 48; the NOx
SIP Call Rule, CAIR, and the Clean Smokestacks Act, which will significantly reduce SO2
emissions from the large electrical generation units with implementation beginning prior to the
2009 attainment year and well in advance of the Federal Clean Air Interstate Rule. The Clean
Smokestacks Act further requires the coal-fired power plants to meet an annual SO2 emissions
cap without an option of emissions trading from outside of North Carolina.
ATTAINMENT TEST RESULTS
A modeled attainment test was applied to the air quality modeling results to determine if the
annual PM2.5 NAAQS will be met by the attainment year 2009. The baseline period for the air
quality modeling was centered on 2002 or the midpoint of the three years used for nonattainment
designations.
For all FRM sites in the Hickory and Triad PM2.5 nonattainment areas, the future annual PM2.5
concentrations derived from the modeled attainment test were less than 15.0 μg/m3 (Table 1).
Therefore, the modeling assessment indicated that both nonattainment areas will attain the
annual PM2.5 NAAQS by 2009.
County FRM Monitoring
Site
2001-2003, Current
Design Value
(μg/m3)
2009, Future Year
Predicted Design
Value
(μg/m3)
The North Carolina Division of Air Quality (NCDAQ) provided a strong set of supplemental
analyses further supporting that the Hickory and Triad PM2.5 nonattainment areas will attain the
annual PM2.5 NAAQS by April 5, 2010. These analyses included evaluating the air quality
modeling from an absolute percentage reduction perspective compared to the annual PM2.5
NAAQS, investigating current air quality data trends along with the emission reductions that
have recently occurred, and considering air quality modeling results from other region and
national modeling exercises.
The NCDAQ believes that the modeling attainment demonstration, in conjunction with the
supplemental analyses, provides the necessary evidence that the Hickory and Triad PM2.5
nonattainment areas will attain the annual PM2.5 NAAQS by the April 5, 2010 attainment date
and furthermore continue to maintain the daily PM2.5 NAAQS. In fact, both nonattainment areas
have already attained the 1997 annual PM2.5 standard with the 2006-2008 ambient air quality
data, one year earlier than required.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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1.1 What is fine particulate matter? ............................................................................................ 1
1.2 What is the National Ambient Air Quality Standard? .......................................................... 2
1.3 Nature of Problem in North Carolina ................................................................................... 2
1.4 Major Contributors to PM2.5 in the North Carolina Nonattainment Areas ........................... 5
1.5 Clean Air Act Requirements ................................................................................................. 6
2.1 PM2.5 Component Species Analysis ..................................................................................... 7
2.2 Attribution of Emissions Sources ......................................................................................... 9
2.3 Clean Air Fine Particulate Implementation Rule Presumptions on Precursor Pollutants ... 12
3.1 Analysis Method ................................................................................................................. 13
3.2 Model Selection .................................................................................................................. 14
3.2.1 Selection of Air Quality Model .................................................................................... 14
3.2.2 Selection of Meteorological Model ............................................................................. 15
3.2.3 Selection of Emissions Processing System .................................................................. 18
3.3 Selection of the Modeling Year .......................................................................................... 19
3.4 Modeling Domains ............................................................................................................. 20
3.4.1 Horizontal Modeling Domain ...................................................................................... 20
3.4.2 Vertical Modeling Domain .......................................................................................... 22
3.5 Baseline Emissions Inventory ............................................................................................. 23
3.5.1 Stationary Point Sources .............................................................................................. 26
3.5.2 Stationary Area Sources ............................................................................................... 27
3.5.3 Off-Road Mobile Sources ............................................................................................ 28
3.5.4 Highway Mobile Sources ............................................................................................. 28
3.5.5 Biogenic Emission Sources .......................................................................................... 29
4.1 Meteorological Model Performance ................................................................................... 30
4.2 Air Quality Model Performance ......................................................................................... 31
4.2.1 Modeling Performance Goals, and Criteria ................................................................. 32
4.2.2 Domain-Wide Model Performance .............................................................................. 33
4.2.3 Nonattainment Area Model Performance .................................................................... 35
4.2.4 Air Quality Model Performance Summary .................................................................. 37
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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5.1 Federal Control Measures ................................................................................................... 39
5.1.1 Tier 2 Vehicle Standards .............................................................................................. 39
5.1.2 Heavy-Duty Gasoline and Diesel Highway Vehicles Standards ................................. 39
5.1.3 Large Non-road Diesel Engines Rule .......................................................................... 39
5.1.4 Non-road Spark-Ignition Engines and Recreational Engines Standard ....................... 40
5.1.5 NOx SIP Call in Surrounding States ............................................................................ 40
5.1.6 Clean Air Interstate Rule.............................................................................................. 40
5.2 State Control Measures ....................................................................................................... 41
5.2.1 Clean Air Bill ............................................................................................................... 41
5.2.2 NOx SIP Call Rule ....................................................................................................... 42
5.2.3 Clean Smokestacks Act ................................................................................................ 42
5.2.4 Open Burning Bans ...................................................................................................... 43
5.2.5 Clean Air Interstate Rule.............................................................................................. 43
6.1 Attainment Test Introduction .............................................................................................. 44
6.2 Attainment Test Results ...................................................................................................... 45
6.3 Supplemental Analyses ....................................................................................................... 45
6.3.1 Air Quality Modeling Metrics ..................................................................................... 46
6.3.2 Other Modeling Results ............................................................................................... 48
6.3.3 Air Quality Trends and Additional Reductions in Emissions ...................................... 49
6.4 Unmonitored Area Analysis ............................................................................................... 51
6.5 Data Access ......................................................................................................................... 53
7.1 Reasonable Available Control Measures ............................................................................ 54
7.2 Reasonable Further Progress .............................................................................................. 55
7.3 Actual Emissions Inventory ................................................................................................ 55
7.4 Periodic Emissions Inventory ............................................................................................. 55
7.5 Permit Program Requirements ............................................................................................ 55
7.6 Other Measures ................................................................................................................... 56
7.7 Compliance with Section 110(a)(2) .................................................................................... 56
7.8 Equivalent Techniques ........................................................................................................ 56
7.9 Contingency Measures ........................................................................................................ 56
8.1 Transportation Conformity ................................................................................................. 58
8.2 Pollutants to be Considered ................................................................................................ 58
8.3 Highway Mobile Source Direct PM2.5 Emissions ............................................................. 59
8.4 Establishing PM2.5 and NOx Motor Vehicle Emission Budgets ......................................... 61
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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Figure 1. Annual PM2.5 Nonattainment Boundaries for North Carolina ....................................... iv
Figure 1.3-1. Annual PM2.5 Nonattainment Boundaries for North Carolina .................................. 3
Figure 1.3-2. PM2.5 Monitoring Sites In North Carolina ................................................................ 3
Figure 1.3-3. PM2.5 FRM Monitoring Sites in the Hickory and Triad Nonattainment Areas ....... 4
Figure 1.3-4. North Carolina PM2.5 Speciation for 2004 ................................................................ 5
Figure 2.1-1. 2002 PM2.5 Speciated Mass Contribution at Hickory Using SANDWICH .............. 8
Figure 2.1-2. 2002 PM2.5 Speciated Mass Contribution at Lexington Using SANDWICH ........... 8
Figure 2.1-3. 2002 PM2.5 Speciated Mass Contribution at Mendenhall Using SANDWICH ........ 9
Figure 2.2-1. Hickory PM2.5 Nonattainment Area SO2 Emissions in 2002 .................................. 10
Figure 2.2-2. Triad PM2.5 Nonattainment Area SO2 Emissions in 2002 ...................................... 11
Figure 2.2-3. North Carolina Total SO2 Emissions in 2002 ......................................................... 11
Figure 3.4.1-1. The MM5 and CMAQ_SOA 36-km Horizontal Domains ................................... 21
Figure 3.4.1-2. VISTAS 12-km Modeling Domain ...................................................................... 21
Figure 4.2.2-1. VISTAS STN Soccer Plots .................................................................................. 33
Figure 4.2.2-2. VISTAS STN Bugle Plots .................................................................................... 34
Figure 4.2.2-3. VISTAS FRM Soccer Plots ................................................................................. 34
Figure 4.2.2-4. VISTAS FRM Bugle Plots ................................................................................... 35
Figure 4.2.3-2. Hickory STN Bugle Plots..................................................................................... 36
Figure 4.2.3-3. Hickory FRM Soccer Plots .................................................................................. 37
Figure 4.2.3-4. Hickory FRM Bugle Plots .................................................................................... 37
Figure 5.2.1-1. North Carolina’s OBDII Test Phase-in Map ........................................................ 41
Figure 6.3.1-1. Area for which the Air Quality Metrics were Applied ........................................ 47
Figure 6.3.1-2. Percentage of Cell in PM2.5 Nonattainment Areas within Concentration
Categories for 2002 and 2009. Table of Actual Values is Presented on the Right. ..................... 48
Figure 6.3.3-1. Annual PM2.5 Average Concentrations for the FRM Monitors in the Hickory and
Triad Nonattainment Areas ........................................................................................................... 50
Figure 6.3.3-2. Annual SO2 Emissions From EGUs In NC .......................................................... 51
Figure 6.4-1. PM2.5 Monitors and Nonattainment Areas with Respect to the VISTAS 12km Grid
Domain ......................................................................................................................................... 52
Figure 6.4-2. PM2.5 Monitors and 2009 Modeled Attainment Spatial Field ................................. 53
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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Table 1. Current And Future Year Predicted Annual PM2.5 Concentrations .................................. v
Table 1.3-1. PM2.5 Concentrations and Design Values for the FRM monitors in the Hickory and
Triad PM2.5 Nonattainment Areas ................................................................................................... 4
Table 3.4.1-1: Vertical Layer Definition For MM5 and CMAQ .................................................. 23
Table 3.5-1. 2002 Annual Emission Summaries .......................................................................... 25
Table 4.2.1-1. Established Model Performance Goals and Criteria for the PM2.5 Component
Species ......................................................................................................................................... 32
Table 5.2.1-1 OBDII Phase-in Effective Dates ............................................................................ 42
Table 6.2-1. Quarterly Mean and Annual Mean PM2.5 Mass Estimates for 2009 ........................ 45
Table 6.3.1-1. Number of Cells within Concentration Bins. Increases (decreases) in the Number
of Cells within the Bins are Noted by Red (Blue) Coloration in the Last Column. ..................... 48
Table 6.3.2-1. USEPA’s CAIR Modeling Results ........................................................................ 49
Table 6.3.3-1. Annual Average PM2.5 Concentrations for the Past 10 Years ............................... 49
Table 6.3.3-2. Three Year Design Values for the FRM Monitors in the Hickory and Triad PM2.5
Nonattainment Areas .................................................................................................................... 50
Table 8.4-1. County Level PM2.5 Highway Mobile Emissions for 2009 ...................................... 61
Table 8.4-2. County Level NOX Highway Mobile Emissions for 2009 ....................................... 61
Table 8.4-3. County Level PM2.5 MVEBs for 2009 ..................................................................... 62
Table 8.4-4. County Level NOx MVEBs for 2009 ....................................................................... 62
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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Appendix A: Policy and Memorandums
Appendix B: Stakeholders Correspondence Regarding Motor Vehicle Emissions Budgets
Appendix C: Air Quality Data
Appendix D: Modeling Protocol
Appendix E: Emissions Inventory Summary
Appendix F: Emissions Inventory Documentation
Appendix G: Emissions Inventory Quality Assurance Project Plan
Appendix H: Emissions Modeling and Related
Appendix I: Meteorological Development Documentation
Appendix J: Model Performance Evaluation
Appendix K: Modeling Results
Appendix L: Attainment Test
Appendix M: Adopted State Measures
Appendix N: Contingency Measures Documentation
Appendix O: Insignificance of NH3 and VOCs to PM2.5 Attainment in North Carolina
Appendix P: Supporting Documentation from VISTAS and ASIP
Appendix Q: Public Notice Report, Comments Received, and Responses
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
1
Fine particulate matter, also known as fine particles and PM2.5, refers to airborne particles less
than or equal to 2.5 micrometers (μm) in diameter. Fine particles are treated as though they are a
single pollutant, but they come from many different sources and are composed of many different
compounds. PM2.5 exposure adversely affects human health, especially respiratory and
cardiovascular systems. Individuals particularly sensitive to PM2.5 exposure include children,
people with heart and lung disease, and older adults.
PM2.5 can be liquid, solid, or can have a solid core surrounded by liquid. PM2.5 can include
material produced by combustion, photochemical reactions, and can contain salt from sea spray
and soil-like particles. Particles are distinguished based on the method of formation. Primary
particles are particles directly emitted into the atmosphere and retain the same chemical
composition as when they were released. Secondary particles are those formed through chemical
reactions involving atmospheric oxygen, water vapor, hydroxyl radical, nitrates, sulfur dioxide
(SO2), oxides of nitrogen (NOx), and organic gases from natural and anthropogenic sources.
PM2.5 can therefore be composed of varying amount of different species, including:
• Sulfates
• Nitrates (usually found in the form of ammonium nitrate)
• Ammonium
• Hydrogen ion
• Particle bound water
• Elemental carbon
• Organic compounds
Primary organic species (from cooking and combustion)
Secondary organic compounds
• Crustal material (includes calcium, aluminum, silicon, magnesium, and iron)
• Sea salt (generally only found at coastal monitoring sites)
• Transitional metals
• Potassium (generally from wood burning or cooking)
The most significant sources of PM2.5 and its precursors are coal-fired power plants, industrial
boilers and other combustion sources. These emissions are often transported over large
distances. Other sources of PM2.5 emissions include mobile sources, area sources, biogenic,
fires, windblown dust, and oceans.
A variety of meteorological and geographic factors influence the concentration levels of fine
particles, including both the regional and local distribution of urbanized areas, primary and
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
2
precursor emissions sources, and natural features such as oceans and forests. PM2.5
concentrations can also be high and exceed the national ambient air quality standards (NAAQSs)
for fine particulate matter at any time of the year. Therefore the United States Environmental
Protection Agency (USEPA) mandates the year round monitoring of PM2.5 concentrations
throughout the country (40 CFR 58.App. D, 4.7).
In 1997, the USEPA promulgated the primary (health) and secondary (welfare) NAAQSs for
PM2.5 (40 CFR 50.7), setting the standard at a 15.0 micrograms per cubic meter (μg/m3) annual
average and at a 65 μg/m3 daily or 24-hour average. A violation of the annual PM2.5 NAAQS
occurs when the annual average PM2.5 concentration averaged over a three consecutive year
period is equal to or greater than 15.1 μg/m3. A violation of the daily PM2.5 NAAQS occurs
when the annual 98th percentile of daily PM2.5 concentration averaged over a three consecutive
year period is equal to or greater than 66 μg/m3. The annual or daily PM2.5 design value for a
nonattainment area is the highest design value for any monitor in that area.
Since the 1977 amendments to the Clean Air Act (CAA), areas of the country that violated the
ambient standard for a particular pollutant were formally designated as nonattainment for that
pollutant. This formal designation concept was retained in the 1990 Amendments (CAAA).
With the implementation of the PM2.5 standard, areas could be designated under Section 172 of
the CAAA (subpart 1) and have five years from designation to attain the standard.
The USEPA designated areas as nonattainment for the annual and daily PM2.5 NAAQSs based
upon air quality monitoring data measured during 2001, 2002 and 2003. The effective date of
nonattainment designations was April 5, 2005.
In North Carolina, there were two areas designated as nonattainment for violating the annual
PM2.5 standard (Figure 1.3-1). All areas of North Carolina met the daily PM2.5 standard. This
PM2.5 attainment demonstration submittal covers the Hickory PM2.5 nonattainment area
(Catawba County) and Greensboro/Winston-Salem/High Point PM2.5 nonattainment area
(referred to as the Triad area and consists of Davidson and Guilford Counties) with respect to the
violations of the annual PM2.5 standard.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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Figure 1.3-2 displays the distribution of the PM2.5 monitoring sites across North Carolina. A
closer view of the PM2.5 federal reference method (FRM) monitoring sites in the Hickory and
Triad PM2.5 nonattainment areas is found in Figure 1.3-3. The Hickory monitoring site is the
only FRM monitor in the Hickory PM2.5 nonattainment area. There are two FRM monitoring
sites, Lexington and Mendenhall, in the Triad PM2.5 nonattainment area.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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Table 1.3-1 contains the quarterly and annual average PM2.5 concentrations for the FRM
monitors in the PM2.5 nonattainment areas for the three-year period used in the nonattainment
designation determinations. Table 1.3-1 also presents the 2001-2003 PM2.5 design value for the
FRM monitors based on these quarterly and annual averages. The historic quarterly, yearly, and
design value air quality data for the FRM monitors in both PM2.5 nonattainment areas can be
found in Appendix C.
County FRM Monitoring
Site Year
1st
Quarter
(Q1)
2nd
Quarter
(Q2)
3rd
Quarter
(Q3)
4th
Quarter
(Q4)
Annual
Average
Design
Value
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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As mentioned in Section 1.1, PM2.5 is composed of many species from varying sources.
Figure 1.3-4 presents the North Carolina statewide averaged PM2.5 speciation data from the
speciation trends network (STN) monitors for the year 2004. The figure presents sulfates (SO4)
and organic carbons (OC) as the main contributors to PM2.5, each with 29%; ammonium (NH4)
contributes 11%; nitrates (NO3) contribute 7%; elemental carbon (EC) is approximately 4%; and
crustal material is 3% of the total PM2.5 mass. The “other” portion of the PM2.5 that accounts for
17% of the mass can be attributed to water (H2O), sea salts, and other trace materials captured
with the STN monitors.
When the annual PM2.5 concentrations in both nonattainment areas are analyzed by the
percentages of their individual component species, a similar distribution of components are
found. The OC and SO4 components each account for approximately one-third of the total PM2.5
mass; NH4 makes up approximately ten percent of the total PM2.5 mass; and the remaining NO3,
EC, crustal material, and particle bound water components each contribute approximately five
percent or less of the total PM2.5 mass. Individual plots of the speciated PM2.5 data (similar to
Figure 1.3-4) from the three PM2.5 monitoring locations in the nonattainment areas can be found
in Appendix C.
The percentages of species contribution fluctuate throughout the year with the most significant
changes to SO4 and NO3. SO4 is more pronounced in the summertime or warm season months
than during the wintertime. NO3 fluctuates from almost undetectable in the summertime to as
much as ten percent contribution of the total PM2.5 mass during the coldest portion of the winter.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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The speciated analysis of the PM2.5 concentrations in the Hickory and Triad PM2.5 nonattainment
areas demonstrates that the OC and SO4 components are the most important portions of the total
PM2.5 mass throughout the year. OC is predominately attributed to biogenic emissions sources.
SO4 is associated with SO2 emissions. When evaluated across North Carolina and also
throughout both nonattainment areas and surrounding regions, the SO2 is primarily from the
point source sector. For this reason, SO2 emissions controls from point sources are believed to
be the most appropriate strategy for addressing the current PM2.5 nonattainment issues for
Hickory and the Triad.
Further details on the nature of the PM2.5 problem in both PM2.5 nonattainment areas are
discussed in Section 2 and can also be found in the Conceptual Description of Fine Particulate
Matter in North Carolina section of Appendix D.1.
Section 172(c) as amended, contains the general requirements for nonattainment areas. These
requirements are listed below and are discussed in more detail in Section 7.
Section 172(c) Nonattainment Plan Provisions
(1) Reasonable available control measures (RACM)
(2) Reasonable further progress (RFP)
(3) Actual emissions inventory and periodic emissions inventory
(4) New source review (NSR)
(5) Permit requirements for new and modified sources
(6) Other measures as may be necessary to provide attainment by specified
attainment date
(7) Compliance with Section 110(a)(2)
(8) Equivalent techniques
(9) Contingency measures
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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As suggested in the Section 1.4, SO2 emissions are believed to be the most appropriate strategy
for addressing the 1997 PM2.5 NAAQS for the Hickory and Triad nonattainment areas. This
finding is based on several factors including:
• An analysis of the percentage contribution of the PM2.5 component species annually
and seasonally within the nonattainment areas
• Attribution of emissions sources to these PM2.5 component species
• Clean Air Fine Particulate Implementation Rule presumptions on precursor pollutants
To fully understand the nature of the PM2.5 nonattainment issues in the Hickory and Triad
nonattainment areas, it is important to analyze the percentage contribution of the individual
PM2.5 component species, both from an annual perspective and seasonally throughout the year.
Unfortunately, the FRM monitoring sites only provide a total mass PM2.5 concentration and do
not provide any information concerning the speciated breakdown of various components. A
separate PM2.5 monitoring network, STN, does allow for the speciation of these components, but
the STN PM2.5 concentration data is not directly comparable to the FRM PM2.5 concentration
data due to slight difference in the monitoring methodology. This creates an issue in using raw
STN PM2.5 data in an attainment demonstration, because it is not absolutely equivalent to the
FRM PM2.5 data of which the nonattainment is based and of which attainment will ultimately be
evaluated.
To address this issue, Neil Frank with the USEPA developed an approach to use the raw STN
PM2.5 data to appropriately estimate the components of PM2.5 as measured by the FRM monitors.
The approach is termed the “ ulfate, djusted itrate, erived ater, nferred arbonaceous
material balance approac ” method or SANDWICH (Frank, 2006). The SANDWICH approach
is discussed in greater detail in Appendix L.
Using the SANDWICH approach, it is now possible to analyze the percentage contribution of the
individual PM2.5 component species relative to the total FRM PM2.5 mass. Figures 2.1-1 through
2.1-3 present the speciated mass contributions of the component species at the Hickory,
Lexington, and Mendenhall monitoring sites, respectively. The speciated mass contributions
displayed are for the 2002 baseline year. Figures 2.1-1 through 2.1-3 illustrate daily speciated
mass contributions for each day of the 2002 calendar year (expressed in Julian days) from left to
right, with the farthest right bar of the charts representing the 2002 annual averaged speciated
mass contributions.
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Julian Day
Mass (μg m-3)
Julian Day
Mass (μg m-3)
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Julian Day
Mass (μg m-3)
From each of the three 2002 PM2.5 speciated mass contribution plots, it is clear that SO4 and OC
are the dominant PM2.5 components throughout the year. SO4 is most pronounced during the
summertime, but remains a reasonably important component of the total PM2.5 mass in any of the
seasons. NH4 and H2O are less dominant than SO4 and OC but are relatively consistent in each
season. EC and crustal material are much less prevalent at any time of the year. Finally, NO3
contributions are almost undetectable in the summertime to as much as ten percent contribution
of the total PM2.5 mass during the wintertime.
Precursor pollutants to PM2.5 can be emitted directly, such as in smoke from a fire, or they can
form from chemical reactions of gases such as SO2, nitrogen dioxide and some organic gases.
Sources of these precursor pollutants include power plants, gasoline and diesel engines, wood
combustion, and high-temperature industrial processes such as smelters and steel mills. Other
sources of the PM2.5 precursor pollutants include mobile sources, area sources, biogenic, fires,
windblown dust, and oceans.
The speciated analysis of the PM2.5 concentrations in the Hickory and Triad PM2.5 nonattainment
areas presented above demonstrates that the OC and SO4 components are the most important
portions of the total PM2.5 mass throughout the year at all three monitoring locations. OC is
predominately attributed to biogenic volatile organic compound (VOC) emissions. SO4 is
associated with SO2 emissions. NH4 can have a variety of sources including both industrial and
natural processes. What little NO3 is present in the PM2.5 nonattainment areas throughout the
year are attributed to NOx from combustion sources. Of all these components and associated
emission sources, SO4 is the only dominant PM2.5 component species found throughout the year
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that is attributed to a set of emissions source (SO2) that are controllable through regulatory
actions by the North Carolina Division of Air Quality (NCDAQ).
When evaluated throughout both nonattainment areas and across North Carolina, SO2 is
primarily from the point source sector. Figures 2.2-1 and 2.2-2 present the SO2 emissions from
the various source sectors in the Hickory and Triad PM2.5 nonattainment areas, respectively.
Both figures are presented on same vertical axis scales to illustrate the significance of a single
point source facility that is inside of the Hickory nonattainment area and immediately adjacent
and upwind of the Triad nonattainment area. The magnitude of the point source sector
completely masks the SO2 emissions from all other source categories. When SO2 emissions by
source category are evaluated across North Carolina (Figure 2.2-3), the point source emissions
are 6 times larger than emissions contained in either of the nonattainment area SO2 emissions
plots. Again, the magnitude of the point source sector completely masks the SO2 emissions from
all other source sectors.
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The USEPA’s Clean Air Fine Particulate Implementation Rule (72 FR 20586), commonly
referred to as the PM2.5 Implementation Rule, guides States as they develop state implementation
plans in response to annual and/or daily PM2.5 nonattainment. It establishes a hierarchy of
precursor pollutants: SO2 is always considered a precursor, NOx is presumptively a precursor,
and VOCs and ammonia are presumed not to be precursors. The State of North Carolina is
following the assertions and presumptions of significant and insignificant precursor pollutants
established in the PM2.5 Implementation Rule in this attainment demonstration. Further
discussion on the significant or insignificance of the various precursor pollutants is discussed in
Appendix O.
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The attainment modeling for the Hickory and Triad PM2.5 nonattainment areas was performed in
conjunction with the regional haze modeling being done by Southeast Regional Planning
Organization (RPO), Visibility Improvement State and Tribal Association of the Southeast
(VISTAS) and the PM2.5 and ozone (O3) modeling being done by the Association of
Southeastern Integrated Planning (ASIP). VISTAS and ASIP are managed by the ten Southeast
states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina,
Tennessee, Virginia and West Virginia). Since the VISTAS/ASIP regional modeling utilized
annual simulations and includes modeling for the attainment year required for the Hickory and
Triad PM2.5 nonattainment areas, the NCDAQ decided to use this modeling for its attainment
demonstration. The sections below outline the methods and inputs used by VISTAS/ASIP for
the regional modeling.
The modeling analysis is a complex technical evaluation that begins by selection of the modeling
system. VISTAS decided to use the following modeling system:
• Meteorological Model: The Pennsylvania State University/National Center for
Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5) is a
nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale
photochemical, fine particulate matter, and regional haze regulatory modeling
studies.
• Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling
system is an emissions modeling system that generates hourly gridded speciated emission
inputs of mobile, non-road mobile, area, point, fire and biogenic emission sources for
photochemical grid models.
• Air Quality Model: The USEPA’s Models-3/ Community Multiscale Air Quality
(CMAQ) modeling system is a “One-Atmosphere” photochemical grid model capable of
addressing O3, particulate matter, visibility and acid deposition at regional scale for
periods up to one year.
Additionally, an historical year is selected to model that represents typical meteorological
conditions in the Southeast when high ozone, high PM2.5 and poor visibility are observed
throughout the region. Once the historical year is selected, meteorological inputs are developed
using the meteorological model. Emission inventories are also developed for the historical year
and processed through the emissions model. These inputs are used in the air quality model to
predict ozone, PM2.5 and visibility, with the results compared to the historic data. The model
performance is evaluated by comparing the modeled predicted data to the historic air quality
data.
Once model performance is deemed adequate, typical baseline and future year emissions are
processed through the emissions model. For this demonstration, the baseline year was 2002,
which corresponds with the same year as the historic meteorology used in the modeling. The
attainment future year the NCDAQ is using for this demonstration is 2009, since the mandatory
attainment date for the Hickory and Triad PM2.5 nonattainment areas is April 5, 2010. The
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attainment date is set prior to the completion of the 2010 calendar year; therefore the attainment
of the NAAQS would have to be met by the end of 2009. These emissions are processed through
the air quality model with the meteorological inputs. The air quality modeling results are used to
determine a relative reduction in future PM2.5 concentrations, which is used in the attainment
demonstration.
The complete modeling protocol used by the NCDAQ for this analysis can be found in
Appendix D.1. For additional reference, the VISTAS/ASIP modeling protocol can be found in
Appendix D.2.
To ensure that a modeling study is defensible, care must be taken in the selection of the models
to be used. The models selected must be scientifically appropriate for the intended application
and be freely accessible to all stakeholders. Scientifically appropriate means that the models
address important physical and chemical phenomena in sufficient detail, using peer-reviewed
methods. Freely accessible means that model formulations and coding are freely available for
review and that the models are available to stakeholders, and their consultants, for execution and
verification at little or no cost.
The following sections outline the criteria for selecting a modeling system that is both defensible
and capable of meeting the study's goals. These criteria were used in selecting the modeling
system used for this modeling attainment demonstration.
For an air quality model to qualify as a candidate for use in an attainment demonstration, a State
needs to show that it meets several general criteria:
• The model has received a scientific peer review.
• The model can be demonstrated applicable to the problem on a theoretical basis.
• Data bases needed to perform the analysis are available and adequate.
• Available past appropriate performance evaluations have shown the model is not biased
toward underestimates or overestimates.
• A protocol on methods and procedures to be followed has been established.
• The developer of the model must be willing to make the source code available to users
for free or for a reasonable cost, and the model cannot otherwise be proprietary.
The air quality model selected for this study was CMAQ version 4.5, which was the most recent
release at the point the attainment modeling exercise started. For more than a decade, the
USEPA has been developing the Models-3 CMAQ modeling system with the overarching aim of
producing a “One-Atmosphere” air quality modeling system capable of addressing ozone, fine
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particulate matter, visibility and acid deposition within a common platform. The original
justification for the Models-3 development emerged from the challenges posed by the CAAA
and the USEPA’s desire to develop an advanced modeling framework for “holistic”
environmental modeling utilizing state-of-science representations of atmospheric processes in a
high performance computing environment. The USEPA completed the initial stage of
development with Models-3 and released the CMAQ model in mid 1999 as the initial operating
science model under the Models-3 framework.
Another reason for choosing CMAQ as the atmospheric model is the ability to do one-atmospheric
modeling. Since the NCDAQ will be using the same modeling exercise for the
ozone and PM2.5 attainment demonstration state implementation plans (SIPs), as well as the
regional haze SIP, having a model that can handle both ozone and particulate matter is essential.
A number of features in CMAQ’s theoretical formulation and technical implementation make the
model well suited for annual PM2.5 modeling.
CMAQ contains three options for treating secondary organic aerosol (SOA), latest being the
Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003 to be a
reversible semi-volatile scheme whereby VOC emissions can be converted to condensable gases
that can then form SOA and then evaporate back into condensable gases depending on
atmospheric conditions.
The CMAQ chemical-transport model processor (CTM) requires the following inputs:
• Three-dimensional hourly meteorological fields that will be generated by the CMAQ
MCIP2.3 processing of the BAMS MM5 output
• Three-dimensional hourly emissions generated by SMOKE
• Initial conditions and boundary conditions
• Topographic information
• Land use categories
• Photolysis rates generated by the CMAQ JPROC processor
The configuration used for this modeling demonstration, as well as a more detailed description of
the CMAQ_SOA (CMAQ version with SOA modification) model, can be found in Appendix
D.1. The resulting model performance evaluation can be found in Appendix J.
Meteorological models, either through objective, diagnostic, or prognostic analysis, extend
available information about the state of the atmosphere to the grid upon which photochemical
grid modeling is to be carried out. The criteria for selecting a meteorological model are based on
both the models ability to accurately replicate important meteorological phenomena in the region
of study, and the model's ability to interface with the rest of the modeling systems, particularly
the air quality model. With these issues in mind, the following criteria were established for the
meteorological model to be used in this study:
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• Non-Hydrostatic Formulation
• Reasonably current, peer reviewed formulation
• Simulates Cloud Physics
• Publicly available at no or low cost
• Output available in I/O API format
• Supports Four Dimensional Data Assimilation
• Enhanced treatment of Planetary Boundary Layer heights for AQ modeling
The non-hydrostatic MM5 model is a three-dimensional, limited-area, primitive equation,
prognostic model that has been used widely in regional air quality model applications. The basic
model has been under continuous development, improvement, testing, and open peer-review for
more than 20 years and has been used worldwide by hundreds of scientists for a variety of
mesoscale studies.
MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical coordinate
similar to that used in many operational and research models. In the non-hydrostatic MM5, the
sigma levels are defined according to the initial hydrostatically balanced reference state so that
the sigma levels are also time-invariant. The gridded meteorological fields produced by MM5
are directly compatible with the input requirements of “one atmosphere” air-quality models using
this coordinate. MM5 fields can be easily used in other regional air quality models with different
coordinate systems by performing a vertical interpolation, followed by a mass-conservation
readjustment.
Distinct planetary boundary layer parameterizations are available for air-quality applications,
both of which represent sub-grid-scale turbulent fluxes of heat, moisture and momentum. One
scheme uses a first-order eddy diffusivity formulation for stable and neutral environments and a
modified first-order scheme for unstable regimes. The other scheme uses a prognostic equation
for the second-order turbulent kinetic energy, while diagnosing the other key boundary layer
terms.
Initial and lateral boundary conditions are specified for real-data cases from mesoscale three-dimensional
analyses performed at 12-hour intervals on the outermost grid mesh selected by the
user. Surface fields are analyzed at three-hour intervals. The GEOS-CHEM global chemical
transport model was run for 2002 to develop the initial and boundary conditions. More details
on the GEOS-CHEM model used in this attainment demonstration can be found in Appendix P .
A Cressman-based technique is used to analyze standard surface and radiosonde observations,
using the National Meteorological Center's spectral analysis, as a first guess. The lateral
boundary data are introduced using a relaxation technique applied in the outermost five rows and
columns of the coarsest grid domain.
Results of detailed performance evaluations of the MM5 modeling system in regulatory air
quality application studies have been widely reported in the literature (e.g., Emery et al., 1999;
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Tesche et al., 2000, 2003) and many have involved comparisons with other prognostic models
such as the Regional Atmospheric Modeling System (RAMS) and the Systems Application
International Mesoscale Model. The MM5 enjoys a far richer application history in regulatory
modeling studies compared with RAMS or other models. Furthermore, in evaluations of these
models in over 60 recent regional scale air quality application studies since 1995, it has generally
been found that the MM5 model tends to produce somewhat better photochemical model inputs
than alternative models.
The databases required for setting up, exercising, and evaluating the MM5 model for the 2002
season consist of various fixed and variable inputs.
• Topography: High resolution (e.g., 30 sec to 5 min) topographic information derived
from the Geophysical Data Center global datasets from the NCAR terrain databases are
available for prescribing terrain elevations throughout the 36-km and 12-km grid domain.
• Vegetation Type and Land Use: Vegetation type and land use information on the 36-km
grid may be developed using the PSU/NCAR 10 min. (~18.5 km) databases while for the
12-km grids, the United States Geological Survey (USGS) data are available.
• Atmospheric Data: Initial and boundary conditions to the MM5 may be developed from
operationally analyzed fields derived from the National Centers for Environmental
Prediction (NCEP) Eta model (40 km resolution) following the procedures outlined by
Stauffer and Seaman (1990). These 3-hour synoptic-scale initialization data include the
horizontal wind components (u and v), temperature, and relative humidity at the standard
pressure levels, plus sea-level pressure and ground temperature. Here, ground
temperature represents surface temperature over land and sea-surface temperature over
water.
• Water Temperature: Water temperatures required on both 36-km and 12-km grids can be
derived from the Eta skin temperature variable. These temperatures are bi-linearly
interpolated to each model domain and, where necessary, filtered to smooth out
irregularities.
• Clouds and Precipitation: While the non-hydrostatic MM5 treats cloud formation and
precipitation directly through explicit, resolved-scale, and parameterized sub-grid scale
processes, the model does not require precipitation or cloud input. The potential for
precipitation and cloud formation enters through the thermodynamic and cloud processes
formulations in the model. The only precipitation-related input required is the initial
mixing ratio field that is developed from the National Weather Service (NWS) and
National Meteorological Center (NMC) datasets.
• Multi-Scale Four Dimensional Data Assimilation (FDDA): The standard "multi-scale"
data assimilation strategy to be used on the 36-km and 12-km grids will objectively
analyze three-dimensional fields produced every 3 hours from the NWS rawinsonde
wind, temperature, and mixing ratio data, and similar analyses are generated every three
hours from the available NWS surface data.
The configuration used for this modeling demonstration, as well as a more detailed description of
the MM5 model, can be found in Appendix I as well as Section 4.6 of the Modeling Protocol
(Appendix D.1).
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The principal criterion for an emissions processing system is that it accurately prepares
emissions files in a format suitable for the photochemical grid model being used. The following
list includes clarification of this criterion and additional desirable criteria for effective use of the
system.
• File System Compatibility with the I/O API
• File Portability
• Ability to grid emissions on a Lambert Conformal projection
• Report Capability
• Graphical Analysis Capability
• MOBILE6 Mobile Source Emissions
• Biogenic Emissions Inventory System version 2 (BEIS-3)
• Ability to process emissions for the proposed domain in a reasonable amount of time.
• Ability to process control strategies
• Little or no cost for acquisition and maintenance
• Expandable to support other species and mechanisms
The Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System was originally
developed at the Micro-computing Center of North Carolina. As with most emissions models,
SMOKE is principally an and not a true
in which emissions estimates are simulated from “first principles”. This means that, with the
exception of mobile and biogenic sources, its purpose is to provide an efficient, modern tool for
converting emissions inventory data into the formatted emission files required by an air quality
simulation model. For mobile sources, SMOKE actually simulates emissions rates based on
input mobile-source activity data, emission factors and outputs from transportation travel-demand
models.
SMOKE was originally designed to allow emissions data processing methods to utilize emergent
high-performance-computing as applied to sparse-matrix algorithms. Indeed, SMOKE is the
fastest emissions processing tool currently available to the air quality modeling community. The
sparse matrix approach utilized throughout SMOKE permits both rapid and flexible processing
of emissions data. The processing is rapid because SMOKE utilizes a series of matrix
calculations instead of less efficient algorithms used in previous systems. The processing is
flexible because the processing steps of temporal projection, controls, chemical speciation,
temporal allocation, and spatial allocation have been separated into independent operations
wherever possible. The results from these steps are merged together at a final stage of
processing.
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SMOKE contains a number of major features that make it an attractive component of the
modeling system. The model supports a variety of input formats from other emissions
processing systems and models. It supports both gridded and county total land use scheme for
biogenic emissions modeling. SMOKE can accommodate emissions files from up to 10
countries and any pollutant can be processed by the system.
For additional information about the SMOKE model please refer to Appendix D.1.
A crucial step to SIP modeling is the selection of the period of time to model to represent current
air quality conditions and to project changes in air quality in response to changes in emissions.
The year 2002 was selected as the base year for several reasons.
The USEPA’s April 2007
(Attainment Modeling
Guidance) identifies specific goals to consider when selecting one or more episodes for use in
modeling to demonstrate the attainment of the NAAQS. The USEPA recommends that episode
selection derive from three principal criteria:
• Simulate a variety of meteorological conditions
• Model time periods in which observed concentrations are close to the appropriate
baseline design value
• Model periods for which extensive air quality/meteorological data bases exist
• Model a sufficient number of days so that the modeled attainment test applied at each
monitor violating the NAAQS is based on multiple days
VISTAS adopted a logical, stepwise approach in implementing the Attainment Modeling
Guidance in order to identify the most preferable, representative modeling year. These steps
include the following:
• Representativeness of Meteorological Conditions: The VISTAS meteorological
contractor (BAMS) identified important meteorological characteristics and data sets in
the VISTAS region directly relevant to the evaluation of candidate annual modeling
episodes. This analysis is discussed in more detail in the project report in Appendix I,
Attachment 1.
• Initial Episode Typing: At the time of selection in 2003, meteorological and air quality
data were available for 2002 for model inputs and model performance evaluation.
VISTAS used Classification and Regression Tree (CART) analyses to evaluate the
candidate modeling years (Douglas et al., 2006). The year 2002 was found to be
representative of conditions in the other years. Subsequently, these analyses were
repeated with the meteorological and air quality monitoring data for 2000 to 2004 to
evaluate how well the 2002 modeling year represented the full 2000-2004 baseline
period. This analysis confirmed that PM2.5 concentrations in 2002 were representative of
the five-year baseline period. The CART analysis is discussed in more detail in
Appendix P.
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• Data Availability: In parallel with the CART analysis, episode characterization analyses,
collaborative investigations by VISTAS states (e.g., North Carolina, Georgia, and
Florida) intensively studied the availability of PM2.5, meteorological, and emissions data
and representativeness of alternative baseline modeling periods from a regulatory
standpoint. Additionally, 2002 was the year that the USEPA was requiring states to
provide emissions inventory data for the Consolidated Emissions Reporting Rule
(CERR), it made sense to use 2002 as the modeling year to take advantage of the 2002
inventory.
• Years to be used by other RPOs: VISTAS also considered what years other RPO would
be modeling, and several had already chosen calendar year 2002 as the modeling year.
After a lengthy process of integrated studies, the episode selection process culminated in the
selection of calendar year 2002 (1 January through 31 December) as the most current,
representative, and pragmatic choice for modeling. All of the USEPA criteria for model year
selection were directly considered in this process together with many other considerations (e.g.,
timing of new emissions or aerometric data deliveries by the USEPA or the states to the
modeling teams).
The CMAQ_SOA model was run in one-way nested grid mode. This allowed the larger outer
domains to feed concentration data to the inner nested domain. One-way nesting is believed to
be appropriate for the generally stagnant conditions experienced during North Carolina’s poor air
quality episodes. Two-way nesting was not considered due to numerical and computational
uncertainty associated with the technique.
The horizontal coarse grid modeling domain boundaries were determined through a national
effort to develop a common grid projection and boundary. A smaller 12-km grid, modeling
domain was selected in an attempt to balance location of areas of interest, such as ozone and fine
particulate matter nonattainment areas. Processing time was also a factor in choosing a smaller
12-km grid, modeling domain.
The coarse 36-km horizontal grid domain covers the continental United States. This domain was
used as the outer grid domain for MM5 modeling with the CMAQ_SOA domain nested within
the MM5 domain. Figure 3.4.1-1 shows the MM5 horizontal domain as the outer most, blue grid
with the CMAQ_SOA 36-km domain nested in the MM5 domain.
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To achieve finer spatial resolution in the VISTAS states, a one-way nested high resolution (12-
km grid resolution) was used. Figure 3.4.1-2 shows the 12-km grid, modeling domain for the
VISTAS region. This is the modeling domain on which the attainment test results are based.
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The vertical grid used in the MM5 modeling primarily defines the CMAQ_SOA vertical
structure. The MM5 model employed a terrain following coordinate system defined by pressure,
using 34 layers that extend from the surface to the 100 millibars (mb). Table 3.4.1-1 lists the
layer definitions for both MM5 and for CMAQ. A layer-averaging scheme is adopted for
CMAQ to reduce the computational cost of the CMAQ simulations. A layer-averaging scheme
was used to generate 19 vertical layers for CMAQ_SOA to reduce the computational cost of the
CMAQ_SOA simulations. The effects of layer averaging were evaluated in conjunction with the
modeling effort and were found to have a relatively minor effect on the model performance
metrics when both the 34 layer and a 19 layer CMAQ_SOA models were compared to ambient
monitoring data. Further discussion on the layer-averaging scheme can be found in Section 5 of
the Modeling Protocol in Appendix D.1.
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MM5 Simulation CMAQ 19 Layers
34 0.000 100 14662 1841 19 0.000 100 14662 6536
29 0.250 325 8127 843 18 0.250 325 8127 2966
25 0.450 505 5160 607 17 0.450 505 5160 1712
22 0.600 640 3448 506 16 0.600 640 3448 986
20 0.700 730 2462 367 15 0.700 730 2462 633
18 0.770 793 1828 259 14 0.770 793 1828 428
16 0.820 838 1400 166 13 0.820 838 1400 329
14 0.860 874 1071 160 12 0.860 874 1071 160
11 0.880 892 911 158
12 0.900 910 753 78 10 0.900 910 753 155
10 0.920 928 598 77 9 0.920 928 598 153
8 0.940 946 445 76 8 0.940 946 445 76
7 0.950 955 369 75 7 0.950 955 369 75
6 0.960 964 294 74 6 0.960 964 294 74
5 0.970 973 220 74 5 0.970 973 220 74
4 0.980 982 146 37 4 0.980 982 146 37
3 0.985 986.5 109 37 3 0.985 986.5 109 37
2 0.990 991 73 36 2 0.990 991 73 36
1 0.995 995.5 36 36 1 0.995 995.5 36 36
0 1.000 1000 0 0 0 1.000 1000 0 0
The CAAA revised many of the provisions of the CAA related to attainment of the NAAQS and
the protection of visibility in mandatory Class I Federal areas (certain national parks and
wilderness areas). These revisions established new emission inventory requirements applicable
to certain areas that were designated nonattainment for certain pollutants. In the case of
particulate matter, the emission inventory provisions are in the general provisions under
Section 172(c)(3).
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There are various types of emission inventories. The first is the actual base year inventory. This
inventory is the base year emissions that correspond to the meteorological data used, which for
this modeling effort is data from 2002. These emissions are used for evaluating the air quality
model performance.
The second type of inventory is the typical base year inventory. This inventory is similar to the
actual base year inventory, except that for sources whose emissions change significantly from
year to year, a more typical emission value is used. In this modeling effort, typical emissions
were developed for the electric generating units (EGUs) and the wildland fire emissions. The air
quality modeling runs using the typical base year inventory are used to calculate relative
reduction factors used in the attainment demonstration test.
The future year base inventory is the third type of inventory and is an inventory developed for
some future year for which attainment of the fine particulate matter standard is needed. For this
modeling project, the future year inventory will be 2009, the last complete year for which the
standard must be attained. It is the future base year inventory that control strategies and
sensitivities are applied to determine what controls might be needed in order to attain and
maintain the annual PM2.5 standard.
Within each type of emission inventory, there are five different emission inventory source
classifications: stationary point and area sources, off-road and on-road mobile sources, and
biogenic sources. Stationary point sources are those sources that emit greater than a specified
tonnage per year, with data provided at the facility level. Electric generating utilities and
industrial sources are the major categories for stationary point sources.
Stationary area sources are those sources whose individual emissions are relatively small, but
due to the large number of these sources, the collective emissions from the source category could
be significant (i.e., dry cleaners, service stations, agricultural sources, fire emissions, etc.).
These types of emissions are estimated on a countywide level.
Non-road (or off-road) mobile sources are equipment that can move but do not use the roadways,
i.e., lawn mowers, construction equipment, railroad locomotives, aircraft, etc. The emissions
from these sources, like stationary area sources, are estimated on a countywide level.
On-road mobile sources are automobiles, buses, trucks, and motorcycles that use the roadway
system. The emissions from these sources are estimated by vehicle type and road type, and are
summed to the countywide level.
Biogenic sources are the natural sources like trees, crops, grasses and natural decay of plants.
The emissions from these sources are estimated at the grid cell level and summarized to the
county level.
For each type of emission inventory and each source classification, the pollutants inventoried
include VOC, NOx, PM2.5, coarse particulate (PM10), ammonia (NH3) and SO2. Table 3.5-1
presents a summary of the actual and typical 2002 annual emissions from the various source
sectors for the counties in the Hickory and Triad PM2.5 nonattainment areas. The full emission
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summaries for all counties in North Carolina and all states in the VISTAS/ASIP region can be
found in Appendix E.
Point Non-road
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Area Mobile
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Emissions reported as tons/year.
Point Non-road
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Area Mobile
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Emissions reported as tons/year.
Point Non-road
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Area Mobile
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Emissions reported as tons/year.
In the sections that follow, a synopsis of the inventories used for each source classifications are
discussed. The detail discussions of the emissions inventory development can be found in
Appendix F. Further information on the emission inventory development for the entire southeast
and the inventories used for other RPOs can be found in Appendix P. Discussion of other input
requirements for SMOKE can also be found in Section 4.6 of the Modeling Protocol
(Appendix D.1).
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North Carolina Attainment Demonstration August 21, 2009
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Point source emissions are emissions from individual sources having a fixed location. Generally,
these sources must have permits to operate, and their emissions are inventoried on a regular
schedule. Large sources having the potential to emit at least 100 tons per year (tpy) of a criteria
pollutant, 10 tpy of a single hazardous air pollutant (HAP), or 25 tpy total HAP are inventoried
annually. Smaller sources have been inventoried less frequently. The point source emissions
data can be grouped as EGU sources and other industrial point sources, also called non-electric
generating units (non-EGUs). Appendix F.1 documents the point source modeling inventory
development in more details
The actual base year inventory for the EGU sources used 2002 continuous emissions monitoring
(CEM) data reported to the USEPA’s Acid Rain program or 2002 hourly emissions data
provided by stakeholders. These data provide hourly emissions profiles for SO2 and NOx that
can be used in air quality modeling. Emissions profiles are used to estimate emissions of other
pollutants based on measured emissions of SO2 and NOx.
Emissions from EGU vary daily and seasonally as a function of variability in energy demand and
utilization and outage schedules. Since emission from EGUs represent a significant portion of
the emission inventory, a typical base year emissions inventory was developed to avoid
anomalies in future year emissions due to variability in meteorology, economic and outage
factors in 2002. This approach is consistent with the Attainment Modeling Guidance. To
develop a typical year 2002 emissions inventory for EGU sources, each unit’s average CEM heat
input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit specific
normalizing factor. This normalizing factor was then multiplied by the 2002 actual emissions.
The heat inputs for the period 2000 through 2004 were used because the modeling current design
values use monitored data from this same 5-year period. If a unit was shut down for an entire
year during the 2000 through 2004 period, the average of the years the unit was operational was
used. If a unit was shut down in 2002, but not permanently shutdown, the emissions and heat
inputs from 2001 (or 2000) were used in the normalizing calculations. For more information
about typical 2002 EGU emissions, please reference to Section 2.1.4 (EGU Analysis) of
Appendix F.1 (Point Source Emissions Inventory (EI) documentation).
As part of the air quality modeling, VISTAS, in cooperation with the other eastern RPOs,
contracted with ICF Resources, L.L.C., to generate future year emission inventories for the
electric generating sector of the contiguous United States using the Integrated Planning Model
(IPM). IPM is a dynamic linear optimization model that can be used to examine air pollution
control policies for various pollutants throughout the contiguous United States for the entire
electric power system. The dynamic nature of IPM enables projection of the behavior of the
power system over a specified future period. Optimization logic in IPM determines the least-cost
means of meeting electric generation and capacity requirements while complying with
specified constraints including air pollution regulations, transmission bottlenecks, and plant-specific
operational constraints. The versatility of IPM allows users to specify which constraints
to exercise, and to populate IPM with their own datasets. For more discussion on how the IPM
data was developed, please refer to Section 3.1.1 (Chronology of the Development of EGU
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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Projections) and Section 3.1.2 (VISTAS/MRPO IPM runs for EGU sources) of Appendix F.1
(Point Source EI documentation).
The IPM modeling runs took into consideration both The Clean Air Interstate Rule (CAIR)
implementation and North Carolina’s Clean Smokestack Act (CSA) requirements for Duke
Power and Progress Energy. The VISTAS States and stakeholders also provided changes for the
following:
• NOx post-combustion control on existing units
• SO2 scrubbers on existing units
• SO2 emission limitations
• Particulate Matter (PM) controls on existing units
• Summer net dependable capacity
• Heat rate for existing units
• SO2 and NOx control plans based on State rules or enforcement settlements
For a detailed discussion about how IPM took consideration for federal, state and source-specific
requirements, please also refer to Appendix F.1.
For the non-EGU sources, the same inventory is used for both the actual and typical base year
emissions inventories. The non-EGU category uses annual emissions as reported under the
CERR for the year 2002. These emissions are temporally allocated to month, day, and hour
using source category code (SCC)-based allocation factors.
The general approach for assembling future year data was to use recently updated growth and
control data consistent with the USEPA’s CAIR analyses. This data was supplemented with
state-specific growth factors and stakeholder input on growth assumptions.
Stationary area sources are sources whose individual emissions are relatively small, but due to
the large number of these sources, the collective emissions could be significant (i.e., combustion
of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by
multiplying an emission factor by some known indicator of collective activity, such as fuel
usage, number of households, or population. Stationary area source emissions are estimated at
the countywide level.
A portion of the area source 2002 base year inventory for North Carolina was developed by the
NCDAQ and provided to the VISTAS/ASIP contractor. The VISTAS/ASIP contractor
calculated the remaining portion of the area source inventory. The sources estimated by the
contractor include emissions from animal husbandry, wild land fires, and particulate matter from
paved and unpaved roads. For the other states within the modeling domain, either state-supplied
data or data reported under CERR for 2002 was used.
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North Carolina Attainment Demonstration August 21, 2009
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The actual base year inventory will serve as the typical base year inventory for all area source
categories except for wild land fires. For wild land fires, a typical year inventory was used to
avoid anomalies in wildfire activity in 2002 compared to longer-term averages. Development of
a typical year fire inventory provided the capability of using a comparable data set for both the
base year and future years. Thus, fire emissions remain the same for air quality modeling in both
the base and any future years. The VISTAS Fire Special Interest Work Group used State records
to ratio the number of acres burned over a longer term period (three or more years, as available
from state records) to 2002. Based on these ratios, the 2002 acreage was then scaled up or down
to develop a typical year inventory.
For categories other than wildland fires, the VISTAS/ASIP contractor generated the future base
year emissions inventory used in the attainment demonstration modeling. Growth factors
supplied from the states or the USEPA’s CAIR emission projections were applied to project the
controlled emissions to the appropriate year. In some cases, the USEPA’s Economic Growth and
Analysis System Version 5 growth factors were used if no growth factor was available from
either the states or the CAIR growth factor files. Appendix F.2 provides a detailed discussion of
the area source inventory.
Off-road (or non-road) mobile sources are equipment that can move but do not use the roadways,
such as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc.
For the majority of the non-road mobile sources, the emissions for 2002 were estimated using the
USEPA’s NONROAD2005c model. For the three source categories not included in the
NONROAD model, i.e., aircraft engines, railroad locomotives and commercial marine, more
traditional methods of estimating the emissions were used. The same inventory is used for both
the actual and typical base year emissions inventories.
For the source categories estimated using the USEPA’s NONROAD model, the model growth
assumptions were used to create the 2009 future year inventory. The NONROAD model takes
into consideration regulations affecting emissions from these source categories. For the four
largest airports in North Carolina, the Federal Aviation Administration’s Terminal Area Forecast
was used to project growth in aircraft emissions. For the commercial marine, railroad
locomotives and the remaining airport emissions, the VISTAS/ASIP contractor calculated the
future growth in emissions using detailed inventory data (both before and after controls) for 1996
and 2010, obtained from the CAIR Technical Support Document. When available, state-specific
growth factors were used. Appendix F.2 provides a detailed discussion of the non-road mobile
source inventory
For onroad vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. Key
inputs for MOBILE include information on the age of vehicles on the roads, the average speeds
on the roads, the mix of vehicles on the roads, any programs in place in an area to reduce
emissions for motor vehicles (such as emissions inspection programs), and temperature.
The MOBILE model takes into consideration regulations that affect emissions from this source
sector. The same MOBILE run is used to represent the actual and typical year emissions for
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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onroad vehicles using input data reflective of 2002. The MOBILE model is then run for the
2009 inventory using input data reflective of that year. The 2002 vehicle miles traveled (VMT),
speeds, vehicle age and vehicle mix data were obtained from the North Carolina Department of
Transportation (NCDOT). For urban areas in North Carolina that run travel demand models
(TDMs), the VMT and speed data from TDMs were used. For a detailed discussion about the
highway mobile source inventory development used in the attainment demonstration modeling,
please refer to Appendix F.3.
Biogenic emissions were prepared with the SMOKE-BEIS3 (Biogenic Emission Inventory
System 3 version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of the Urban Airshed
Model (UAM)-BEIS3 model. Modifications include use of MM5 data, gridded land use data,
and improved emissions characterization. The emission factors that are used in SMOKE-BEIS3
are the same as the emission factors as in UAM-BEIS3. The basis for the gridded land use data
used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Database
version 3 (BELD3) provided by the USEPA. A separate land classification scheme, based upon
satellite (AVHRR, 1 km spatial resolution) and census information aided in defining the forest,
agriculture, and urban portions of each county.
The base year biogenic emissions are used for the typical and future year modeling. This is a
common practice in air quality modeling since the same meteorology is used for all the modeling
years and the biogenic emissions are very dependent on the meteorology. Variation in these
emissions could impact the control strategies needed to demonstrate attainment. Therefore, these
emissions are kept constant.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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There are many aspects of model performance. This section will focus primarily on the methods
and techniques recommended by the USEPA for evaluating the performance of the air quality
model. Before the air quality model can be fully evaluated, an understanding of the
meteorological modeling performance is needed to understand potential biases and errors that
may be passed from the meteorological model directly into the air quality model. The
meteorological modeling evaluation is fully documented in Appendix I and is briefly
summarized in Section 4.1. The air quality modeling evaluation is fully documented in
Appendix J and is briefly summarized in Section 4.2.
Generally speaking, the meteorological modeling performance was quite good at both the 36-km
and 12-km grid resolutions. Synoptic features were routinely accurately predicted and the
meteorological model showed considerable skill in replicating the state variables (e.g.
temperature, mixing ratio, relative humidity, wind speed and direction, cloud cover, and
precipitation). The meteorological modeling performance statistics fell within expected and
acceptable ranges of error during the majority of the 2002 modeled year.
The meteorological modeling performance for North Carolina was very similar to the
performance for the VISTAS/ASIP region for the 12-km modeling domain. Again, large-scale
meteorological patterns were accurately predicted. The meteorological model demonstrated
substantial skill throughout the entire year and was especially skillful during the summertime
season from May through September.
For the North Carolina portion of the 12km modeling domain, the temperature bias was negative
for the entire year. The months of April through September had an average bias closer to zero
(- 0.1 Kelvin) than the fall and winter months. Overall, the diurnal pattern was captured very
well, with only a slight cool bias in the daytime, and a slight warm bias overnight.
Modeled mixing ratio followed observed trends fairly well. There was a slight low bias in the
morning through the early afternoon, and a high bias in the late afternoon and at overnight. The
bias values were generally near zero for most of the year (within ± 0.25 g/kg). Another
atmospheric moisture parameter, relative humidity, also showed a high bias in the daytime with a
low bias at night. Relative humidity biases tracked with temperature biases (higher in fall and
winter, lower in spring and summer), as it is a function of temperature. Precipitation has a
negative bias in the late fall (October through December) and a positive bias in the spring to
summer period. Though the model has a tendency to overestimate the amount of spring and
summertime precipitation, the spatial coverage of measurable precipitation is estimate fairly
well.
Wind speed had approximately 0.5 m/s (meters per second) high bias during the daytime hours,
and approximately 1 m/s high bias at overnight. This high bias is in part due to the inability of
the model to produce calm, or no wind condition. The models always have some level of winds
present. This is further aggravated by the fact that observation networks have a “starting
thresholds” for their wind speed instrumentation. The instruments need winds in excess of
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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1.34 m/s in order to register. As a result, wind speeds less than 1.34 m/s are reported as “calm”.
When omitting calm observations, the positive bias improves to between 0.2 to 0.6 m/s.
The meteorological model performance could have impact on the air quality model performance.
For example, the low temp bias in winter could impact the nitrate chemistry and allow for more
nitrate formation during this period. Moisture biases may impact secondary aerosol formation,
though it is questionable to what extent this may happen. Additionally, the under prediction of
precipitation in the late fall (October through December) may lead to over prediction of PM2.5.
Conversely the over prediction precipitation amounts in the April to September time frame may
lead to under prediction of total PM2.5 concentrations. Also, the slightly higher modeled wind
speeds could lead to additional dispersion of pollutants and ultimately to an under-prediction of
PM2.5 in the modeling results.
Overall, the NCDAQ believes that the meteorological model performance is adequate for this
modeling exercise and should produce credible inputs for the air quality modeling for the
attainment demonstration for the Hickory and Triad PM2.5 nonattainment areas.
Model performance analysis was completed with the final emissions inventory for the entire
VISTAS/ASIP 36km domain. For the full model performance evaluation for the 36-km domain,
please see the ASIP Technical support Document in Appendix P.
The remainder of the discussion of model performance presented here focuses on the comparison
of observational data from the FRM and STN monitoring sites and model output data from the
2002 actual annual air quality modeling. The evaluation primarily focuses on the air quality
model’s performance with respect to individual components of PM2.5, as good model
performance of the component species dictates good model performance of total or reconstituted
fine particulate matter. Model performance of the total fine particulate matter will also be
provided as a means to discuss the overall model performance for this Implementation Plan.
The air quality model evaluation focused on both the FRM and STN monitors across the state.
Designations were based on FRM monitors, and calculations of future design values are based on
current design value information from these sites. Since future attainment demonstrations hinge
on the model representing the FRM sites well, it follows that model performance for these sites
should be evaluated. STN data was also evaluated as this data is used to speciate the FRM data
so component based relative response factors can be calculated for each FRM monitoring site.
More detailed information on the attainment test process is described in Appendix L.
Only a brief summary of the model performance evaluation for the 12-km grid domain will be
discussed in the subsections to follow. For the full model performance evaluation for the 12-km
grid domain, please refer to Appendix J. A full model performance, including an analysis of
model statistics, scatter plots, time series, and stacked bar charts for the 12-km VISTAS/ASIP
domain, all North Carolina monitoring sites collectively, and individually for the monitoring
sites within the nonattainment area, please refer to Appendix J.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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In 2004, VISTAS/ASIP established model performance goals and criteria for components of fine
particle mass (Table 4.2.1-1) based on previous model performance for ozone and fine particles.
The Attainment Modeling Guidance for fine particulate matter at the time noted that PM models
might not be able to achieve the same level of performance as ozone models. VISTAS’s
evaluation considered several statistical performance measures and displays. Fractional bias and
mean fractional error were selected as the most appropriate metrics to summarize model
performance; other metrics were also calculated and are included for FRM and STN monitors in
the full model performance evaluation found in Appendix J.
<15 percent <35 percent Goal for PM2.5 model performance based on ozone
model performance, considered excellent performance
<30 percent <50 percent Goal for PM2.5 model performance, considered good
performance
<60 percent <75 percent Criteria for PM2.5 model performance, considered
average performance. Exceeding this level of
performance indicates fundamental concerns with the
modeling system and triggers diagnostic evaluation.
An additional way to evaluate model performance statistics is to visualize performance based on
these fractional bias and mean fractional error goals via “soccer plots” and “bugle plots”. The
soccer plot is so named because the dotted lines resemble a soccer goal. The soccer plot is useful
as both bias and error are shown on a single plot. As bias and error approach zero, the points are
plotted closer to or within the “goal”, represented here by the dashed boxes.
The bugle plot, named for the shape formed by the criteria and goal lines. The bugle plots are
shaped as such because the goal and criteria lines are adjusted based on the average
concentration of the observed species. As the average concentration becomes smaller, the
criteria and goal lines become larger to adjust for the model’s poor ability to predict at low
concentrations.
The analysis of bugle plots demonstrated that greater emphasis should be placed on performance
of those components with the greatest contribution to PM2.5 mass (e.g. SO4 and OC) and that
greater bias and error could be accepted for components with smaller contributions to total PM2.5
mass (e.g. EC, NO3, and soil). The soccer plots and bugle plots have been included as suggested
model performance evaluation displays in the Attainment Modeling Guidance.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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As a summary of model performance, soccer and bugle plots for the all of the VISTAS STN and
FRM monitors are included here. Plots have been developed for the average monthly modeled
concentrations and the performance statistics for all of the PM2.5 component species (SO4, NO3,
NH4, OC, and EC) and reconstructed PM2.5 total mass from the STN monitoring sites
(Figures 4.2.2-1 and 4.2.2-2), as well as the total PM2.5 mass from the FRM monitoring sites
(Figures 4.2.2-3 and 4.2.2-4).
The soccer plots for monthly average component performance for all the VISTAS/ASIP STN
sites shows generally good model performance for most species of PM2.5 and total PM2.5. The
exception is the prediction of NO3 values, which most values fall outside the criteria goal
(Figure 4.2.2-1). There are a few months that fall on the criteria level goal, which is better seen
in the zoomed view presented in the image on the right in Figure 4.2.2-1. However, when the
very low concentration of NO3 is taken into consideration, as presented in the bugle plots
(Figures 4.2.2-2), NO3 performance largely falls within the criteria and goal model performance
lines. One can still note a general tendency for under prediction in NO3, and other species in
right hand image in Figure 4.2.2-2, which leads to a slight under prediction in total reconstructed
PM2.5.
Monthly total PM2.5 concentration performance at all the VISTAS/ASIP FRM monitors largely
falls within goal level thresholds, with only three months falling just outside goal level
performance (Figure 4.2.2-3). Figure 4.2.2-4 suggests a slight negative bias in PM2.5 prediction
for most of the year, with mean fractional error values remaining within goal levels across the
year.
STN VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
STN VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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STN VISTAS Sites CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Bias
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
STN VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
FRM VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
FRM VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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FRM VISTAS Sites CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Bias
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
FRM VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Error
PM2.5
Criteria
Goal
Overall, the general tendency is for the model to have some difficulty in predicting NO3, as the
monthly average values tend to fall outside the criteria goals for performance in the soccer plots.
Part of this under prediction lies in the fact that NO3 are generally found in low concentration
across the southeast, and the model generally has difficulties representing any compound with
low atmospheric concentrations. The bugle plots are more encouraging with NO3 performance,
as these plot take into consideration the concentration of the component when evaluating
performance. The bugle plots show all components and total PM2.5 falling within criteria level,
or better, of model performance goals. The weaker performance of NO3 accounts for the slight
negative bias in the both the total reconstructed PM2.5 mass from STN sites as well as FRM total
PM2.5 data.
The statistical metrics were calculated for the Hickory (Catawba County) and Hattie Avenue
(Forsyth County) STN monitors to demonstrate model performance for the components of PM2.5
in and near the PM2.5 nonattainment areas. Model performance statistics for the STN sites were
calculated on a component and total PM2.5 basis for the entire base year.
Model performance statistics were also calculated collectively for the FRM monitors within the
VISTAS 12-km domain, as well as individually for the 3 FRM monitors in the nonattainment
areas (Hickory, Lexington, and Mendenhall) to demonstrate the model’s ability to replicate total
PM2.5 mass at these sites. Summaries and statistical tables for the STN monitoring sites and
FRM monitoring sites can be found in Appendix J.
As a summary of model performance at the nonattainment area level, the soccer and bugle plots
for the Hickory STN (Figure 4.2.3-1 and 4.2.3-2) and FRM monitor (Figure 4.2.3-3 and 4.2.4-4)
follow. Plots have been developed for the average monthly concentrations of PM2.5 and its
component species at the STN sites, and for total PM2.5 from FRM monitors for all North
Carolina STN sites collectively and other the monitoring sites within the PM2.5 nonattainment
areas are included in Appendix J.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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Monthly average component concentration performance at the Hickory STN site is similar to
overall 12-km VISTAS domain and North Carolina statewide model performance. Nitrate
generally falls outside of suggested criteria model performance goals. Some under prediction of
organic carbon values is present, but this is in line with the overall model performance seen
across North Carolina. Overall, the PM2.5 model performance was within criteria level, if not
within the goal level thresholds.
STN Hickory CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
STN Hickory CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
STN Hickory CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Bias
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
STN Hickory CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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FRM Hickory CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
FRM Hickory CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
FRM Hickory CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 5.0 10.0 15.0 20.0 25.0
Average Concentration (μg/m3)
Mean Fractional Bias
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
FRM Hickory CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 5.0 10.0 15.0 20.0 25.0
Average Concentration (μg/m3)
Mean Fractional Error
PM2.5
Criteria
Goal
Overall, the model performance for North Carolina through the 2002 baseline modeling year is
reasonable good. For the most part, mean normalized bias and mean normalized gross error are
within the recommended limits for good model performance for most of component species as
well as total PM2.5 mass. Overall performance was good for sulfate and organic carbon, which
are the largest constituents of PM2.5 for North Carolina. Nitrate performance was less than ideal,
especially during the summer months. This is likely due to the generally low atmospheric
concentrations seen in North Carolina. When the performance is weighted by the concentration,
as in the bugle plots, the performance metrics indicate better model performance. The model
also does a good job capturing PM2.5 component and total concentrations through various
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episode-clean out cycles (see Section 5, Appendix J). Overall, the NCDAQ believes that the
model performance is well within the limits of acceptable performance established in the
Attainment Modeling Guidance.
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Several control measures already in place or being implemented over the next few years will
reduce stationary point, highway mobile, and non-road mobile sources emissions. The Federal
and State control measures that have impacts on air quality in North Carolina were modeled for
the attainment year and are discussed in the sections below. Although all the control listed
below may not directly reduce PM2.5 concentrations in North Carolina, the modeling assessment
in this submittal was based on one atmosphere modeling completed for ozone and fine
particulate matter attainment demonstrations and regional haze plans.
Federal Tier 2 vehicle standards will require all passenger vehicles in a manufacturer’s fleet,
including light-duty trucks and Sport Utility Vehicles (SUVs), to meet an average standard of
0.07 grams of NOx per mile. Implementation began in 2004, with full compliance required 2007.
The Tier 2 standards will also cover passenger vehicles over 8,500 pounds gross vehicle weight
rating (the larger pickup trucks and SUVs), which are not covered by the current Tier 1
regulations. For these vehicles, the standards will be phased in beginning in 2008, with full
compliance required by 2009. The new standards require vehicles to be 77% to 95% cleaner
than those on the road today. The Tier 2 rule also reduced the sulfur content of gasoline to 30
parts per million (ppm) starting in January of 2006. Most gasoline sold in North Carolina prior
to January 2006 had a sulfur content of about 300 ppm. Sulfur occurs naturally in gasoline, and
interferes with the operation of catalytic converters on vehicles, which results in higher NOx
emissions. Lower-sulfur gasoline is necessary to achieve the Tier 2 vehicle emission standards.
New USEPA standards designed to reduce NOx and VOC emissions from heavy-duty gasoline
and diesel highway vehicles began to take effect in 2004. The second phase of the standards and
testing procedures, which began in 2007, will reduce particulate matter from heavy-duty
highway engines, and will also reduce highway diesel fuel sulfur content to 15 ppm since the
sulfur damages emission control devices. The total program is expected to achieve a 90%
reduction in PM emissions and a 95% reduction in NOx emissions for these new engines using
low sulfur diesel, compared to existing engines using higher-content sulfur diesel.
In May 2004, the USEPA promulgated new rules for large non-road diesel engines, such as those
used in construction, agricultural, and industrial equipment, to be phased in between 2008 and
2014. The non-road diesel rules also reduce the allowable sulfur in non-road diesel fuel by over
99%. Non-road diesel fuel currently averages about 3,400 ppm sulfur. The rule limits non-road
diesel sulfur content to 500 ppm by 2006 and 15 ppm by 2010. The combined engine and fuel
rules would reduce NOx and PM emissions from large non-road diesel engines by over 90%,
compared to current non-road engines using higher-content sulfur diesel.
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The new standard, effective in July 2003, regulates NOx, hydrocarbons (HC) and carbon
monoxide (CO) for groups of previously unregulated non-road engines. The new standard
applies to all new engines sold in the United States and imported after these standards begins and
applies to large spark-ignition engines (forklifts and airport ground service equipment),
recreational vehicles (off-highway motorcycles and all-terrain-vehicles), and recreational marine
diesel engines. The regulation varies based upon the type of engine or vehicle.
The large spark-ignition engines contribute to ozone formation and ambient CO and PM levels in
urban areas. Tier 1 of this standard was implemented in 2004 and Tier 2 started in 2007. Like
the large spark-ignition, recreational vehicles contribute to ozone formation and ambient CO and
PM levels. For the off-highway motorcycles and all-terrain-vehicles, the new exhaust emissions
standard was phased-in. Fifty percent of model year 2006 engines had to meet the standard, and
for model year 2007 and later, all of the engines have to meet the standard. Recreational marine
diesel engines over 37 kilowatts are used in yachts, cruisers, and other types of pleasure craft.
Recreational marine engines contribute to ozone formation and PM levels, especially in marinas.
Depending on the size of the engine, the standard began phasing-in in 2006.
When all of the non-road spark-ignition and recreational engine standards are fully implemented,
an overall 72% reduction in HC, 80% reduction in NOx, and 56% reduction in CO emissions are
expected by 2020. These controls will help reduce ambient concentrations of ozone, CO, and
fine PM.
In October 1998, the USEPA made a finding of significant contribution of NOx emissions from
certain states and published a rule that set ozone season NOx budgets for the purpose of reducing
regional transport of ozone (63 FR 57356). This rule, referred to as the NOx SIP Call, required
ozone season controls to be put on utility and industrial boilers, as well as internal combustion
engines, in 22 states in the Eastern United States. A NOx emissions budget was set for each state
and the states were required to develop rules that would assure that each state met its budget. A
NOx trading program was established, allowing sources to buy credits to meet their NOx budget
as opposed to actually installing controls. The emission budgets were to be met by the beginning
of 2004. Even with the trading program, the amount of ozone season NOx emissions has
decreased significantly in and around North Carolina.
On May 12, 2005, the USEPA promulgated the “Rule To Reduce Interstate Transport of Fine
Particulate Matter and Ozone (Clean Air Interstate Rule); Revisions to Acid Rain Program;
Revisions to the NOx SIP Call”, referred to as CAIR. This rule established the requirement for
States to adopt rules limiting the emissions of NOx and sulfur dioxide (SO2) and a model rule for
the states to use in developing their rules. The purpose of the CAIR is to reduce interstate
transport of precursors of fine particulate and ozone.
The CAIR applies to (1) any stationary, fossil-fuel-fired boiler or stationary, fossil-fuel-fired
combustion turbine serving at any time, since the start-up of a unit’s combustion chamber, a
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generator with nameplate capacity of more than 25 Megawatt hours (MW) producing electricity
for sale and (2) for a unit that qualifies as a cogeneration unit during the 12-month period starting
on the date that the unit first produces electricity and continues to qualify as a cogeneration unit,
a cogeneration unit serving at any time a generator with nameplate capacity of more than 25 MW
and supplying in any calendar year more than one-third of the unit’s potential electric output
capacity or 219,000 MW, whichever is greater, to any utility power distribution system for sale.
This rule provides annual state caps for NOx and SO2 in two phases, with the Phase I caps for
NOx and SO2 starting in 2009 and 2010, respectively. Phase II caps become effective in 2015.
The USEPA is allowing the caps to be met through a cap and trade program if a state chooses to
participate in the program. When fully implemented, the CAIR will reduce SO2 emissions in the
eastern United States by over 70 percent and NOx emissions by over 60 percent from 2003
levels. Due to Court challenges of CAIR in 2008, the USEPA will be making changes to this
program by 2011. However, the existing CAIR rules will remain in place until the USEPA
promulgates changes to the program.
North Carolina has adopted a number of regulations and legislation to address pollution issues
across the State. These include the Clean Air Bill, the NOx SIP Call Rule, the CSA, the Open
Burning Rule, and the CAIR. All of these regulations were modeled in the attainment
demonstration. These regulations are summarized below and the actual regulations and
legislation can be viewed in Appendix M.
The 1999 Clean Air Bill expanded the vehicle emissions inspection and maintenance program in
North Carolina from 9 counties to 48 counties between July 1, 2002 and January 1, 2006 (Figure
7.2.1-1). Vehicles are tested using the onboard diagnostic system (OBDII) test, an improved
method of testing for pollutant emissions.
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The effective dates for the counties in the Hickory and Triad PM2.5 nonattainment area are listed
in Table 5.2.1-1 below.
County Date
Catawba July 1, 2003
Davidson July 1, 2003
Guildford July 1, 2002
In response to the USEPA’s NOx SIP call, North Carolina adopted rules to control the emissions
of NOx from large stationary combustion sources. These rules cover (1) fossil fuel-fired
stationary boilers, combustion turbines, and combined cycle systems serving a generator with a
nameplate capacity greater than 25 MW and selling any amount of electricity, (2) fossil fuel-fired
stationary boilers, combustion turbines, and combined cycle systems having a maximum
design heat input greater than 250 million British thermal units per hour, and (3) reciprocating
stationary internal combustion engines rated at equal or greater than 2400 brake horsepower
(3000 brake horsepower for diesel engines and 4400 brake horsepower for dual fuel engines).
As part of the NOx SIP call, the USEPA rules established a NOx budget for sources in North
Carolina and other states.
Besides amending existing NOx rules and adopting new NOx rules specifically to address the
USEPA NOx SIP call, the North Carolina rules also require new sources to control emissions of
NOx. The objective of this requirement is (1) to aid in meeting the NOx budget for North
Carolina for minor sources and (2) to aid in attaining and maintaining the ambient air quality
standard for ozone in North Carolina.
North Carolina’s NOx SIP Call rule was predicted to reduce summertime NOx emissions from
power plants and other industries by 68% by 2006. In October 2000, the North Carolina
Environmental Management Commission (EMC) adopted rules requiring the reductions. In
2009, the North Carolina NOx SIP Call program was replaced with the North Carolina’s CAIR
rule, which is discussed below in Section 5.2.5.
In June 2002, the North Carolina General Assembly enacted the CSA, which requires coal-fired
power plants in North Carolina to reduce annual NOx emissions by 77% by 2009. These power
plants must also reduce annual sulfur dioxide emissions by 49% by 2009 and by 73% by 2013.
It is significant to note that this law sets a cap of NOx and SO2 emissions for the State, which the
public utilities cannot meet by purchasing emissions credits. The CSA reduces NOx emissions
beyond the requirements of the NOx SIP Call Rule. One of the first state laws of its kind in the
nation, this legislation provides a model for other states in controlling multiple air pollutants
from older coal-fired power plants.
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The rule adopted by the EMC in June 2004 is aimed at reducing emissions that contribute to
ozone and particle pollution when the air quality is expected to be poor. The ban is triggered on
"air quality action days," when the NCDAQ or local air programs forecast Code Orange, Red or
worse ozone conditions for a particular metro area. The following counties in the Hickory area
are subject to this rule Alexander Catawba Southeastern Burke and Southeastern Caldwell
counties. The following counties in the Triad area are subject to this rule Alamance Caswell
Davidson Davie Forsyth Guilford Randolph Rockingham and Stokes counties.
In response to the USEPA’s CAIR, the NCDAQ developed a state CAIR. Under the USEPA’s
rule, North Carolina has caps as follows:
• Annual NOx: 62,183 tons for 2009-2014 and
51,819 tons for 2015 and each year thereafter;
• Ozone season NOx: 28,392 tons for 2009-2014 and
23,660 tons for 2015 and each year thereafter;
• Annual SO2: 137,342 tons for 2010-2014 and
96,139 tons for 2015 and each year thereafter.
The State’s NOx allocations have been distributed among the covered facilities. The USEPA
will determine the SO2 allocations, which are based on the acid rain program. For the most part
the proposed rules incorporate the USEPA’s model rule. The USEPA’s model rule for
definitions; permitting; monitoring, reporting, and record keeping; trading and banking;
designated representative; opt-in provision, and new source growth are incorporated by
reference.
The rule requires the EMC to periodically review the allocations in 2010 and every five years
thereafter and to decide whether to reallocate. This rule does not preclude the EMC from
adopting additional emission reduction requirements for covered sources if necessary to attain or
maintain an ambient air quality standard.
The EMC adopted North Carolina’s CAIR on March 9, 2006 and the rule became effective
July 1, 2006. Due to the Court challenges of CAIR in 2008, the USEPA will be making changes
to this program soon. However, the existing CAIR rules will remain in place until the USEPA
promulgates changes to the program.
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An attainment demonstration consists of (a) analyses that estimate whether selected emissions
reductions will result in ambient concentrations that meet the NAAQS, and (b) an identified set
of control measures which will result in the required emissions reductions. The necessary
emission reductions for both of these attainment demonstration components may be determined
by relying on results obtained with air quality models.
Section 3.0 of the Attainment Modeling Guidance recommends applying both a modeled
attainment test and a subsequent screening test (or unmonitored area analysis) to the air quality
modeling results to determine if the annual PM2.5 NAAQS will be met. Additional technical or
corroboratory analyses may also be used as part of a “supplemental analysis” or a more stringent
“weight of evidence” determination to supplement the modeled attainment test and to further
support a demonstration of attainment of the annual PM2.5 NAAQS.
This section does not present a modeled attainment test or a subsequent screening test with
respect to the daily PM2.5 NAAQS, because all portions of North Carolina were initially
designated as attaining the daily PM2.5 standard. Continued attainment of the daily PM2.5
NAAQS is projected and assumed due to the widespread reductions in SO2 and NOx emissions
already discussed in Section 5 and the modeling projections discussed later in this Section that
demonstrate significant decreases in PM2.5 concentrations into the future.
The purpose of a modeling assessment is to determine if control strategies currently being
implemented (“on the books”) and proposed control strategies will lead to attainment of the
NAAQS for PM2.5 by the attainment year of 2009. The modeling is applied in a relative sense,
similar to the 8-hour ozone attainment test. However, the PM2.5 attainment test is more
complicated and reflects the fact that PM2.5 has many components. In the test, ambient PM2.5 is
divided into major components, with a separate relative response factor (RRF) and future design
value (DVF) calculated for each of the PM2.5 components. Since the attainment test is calculated
on a per species basis, the attainment test for PM2.5 is referred to as the Speciated Modeled
Attainment Test (SMAT). In its entirety, SMAT consists of four basic steps.
First, the observed quarterly mean PM2.5 and quarterly mean composition for each monitor is
calculated. This is achieved by multiplying the monitored quarterly mean concentration of PM2.5
from FRM monitors by the monitored fractional composition of PM2.5 species for each quarter
(e.g., (20% sulfate) x (15.0 μg/m3 PM2.5 mass) = 3.0 μg/m3 sulfate mass).
The monitored quarterly mean concentration of PM2.5 from FRM monitors are the 5 year
baseline design values (DVB) that are the result of averaging the 3 current design values (DVC)
that straddle the modeling base year. The fractional composition of PM2.5 species is derived
from STN monitoring site data that has been processed by the “sulfate, adjusted nitrate, derived
water, inferred carbonaceous material balance approach”, or SANDWICH method, so STN and
FRM masses are equivalent. The mean composition derived from the SANDWICH method
includes the percent of PM2.5 that can be attributed to SO4, NO3, OC, EC, other primary
inorganic particulates (or crustal materials), NH4, and particle bound water (PBW).
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The second step is to use model results to derive component specific RRF for each monitor for
each quarter. The RRF is basically the ratio of the model’s future projections to the baseline
current projections. For each component, the future year modeled quarterly mean concentration
predicted near the monitoring site divided by the base year modeled quarterly mean
concentration predicted near the monitoring site.
For the third step, the component specific RRFs are applied to the observed air quality
concentrations to project quarterly species estimates. For each quarter, the current quarterly
mean component concentration (step 1) are multiplied by the component-specific RRF obtained
in step 2. This leads to an estimated future quarterly mean concentration for each component.
The fourth step sums the quarterly components to get a quarterly mean PM2.5 value. These
quarterly mean values are then averaged to produce a future year annual average PM2.5 estimate,
or future design value (DVF), for each FRM monitoring site. This final value is then compared
to the NAAQS (15.0 μg/m3) to determine if attainment is reached. For a more detailed
discussion of SMAT and the data at each step for the monitors in the nonattainment areas, see
Appendix L.
The goal of the SMAT process is to sum the quarterly mean PM2.5 components to get annual
mean PM2.5 values. Table 6.2-1 displays the quarterly mean concentration and annual mean
future design values (DVFs) estimates for 2009 for the FRM sites in the North Carolina PM2.5
nonattainment areas.
These 2009 annual DVFs are the final product of the SMAT process and are then compared to
the NAAQS (15.0 μg/m3) to determine if attainment goals will be reached. Since the values at
the FRM site in both the nonattainment areas are less than 15.0 μg/m3, all areas have passed the
attainment test portion of the attainment demonstration.
The Attainment Modeling Guidance asserts that all attainment demonstrations should be
accompanied by supplemental analysis that further supports the modeling conclusions. This
supplemental analysis can include additional analyses of air quality, emissions and
meteorological data, and consider modeling outputs other than the results of the attainment test.
If the attainment test results fall short of the standard, the results of corroboratory analyses may
be used in a weight of evidence determination (WOE) to show that attainment is likely despite
modeled results, which may be inconclusive.
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The Attainment Modeling Guidance defines the guidelines for supplemental analysis/WOE for
the annual PM2.5 standard as follows:
- Site with a DVF less than 14.5 μg/m3 should submit basic supplemental analysis to
confirm the outcome of the model attainment test.
- Sites with a DVF between 14.5 and 15.5 μg/m3 should submit a weight of evidence
demonstration to aggregate supplemental analysis to support the model attainment
demonstration.
- Sites with a DVF greater than or equal to 15.5 μg/m3 should consider additional control
measure to ensure attainment, as more qualitative analysis is unlikely to attainment.
All North Carolina PM2.5 nonattainment areas have DVFs lower than 14.5μg/m3, making the
following section an examination of supplemental analysis to corroborate modeling results,
rather than a WOE analysis to show attainment.
Section 7.1 of the Attainment Modeling Guidance suggests several additional modeling exercises
that can be performed as part of supplemental analysis. One of the metrics that can be
considered as part of this type of additional analysis is the calculation of the percent change in
number of grid cells greater than or equal to 15 μg/m3 within the nonattainment area.
For the Hickory and Triad nonattainment areas, the cell counts of modeling data were tallied
from both the 2002 baseline and the 2009 attainment year modeling run for a subset of the
highest days from the base year. This was done in order to quantify the reduction of PM2.5 on
our highest days through out the year, and not just based on a single annual average from the
modeling. This subset of days included all days with a 24-hour PM2.5 concentration greater than
30 μg/m3 at any of the monitoring sites in either nonattainment area, as well as the four days with
the highest average daily values from each quarter. This selection process identified 28 days for
presentation and coincides with the days used in the model performance evaluation (Appendix J)
and in the model results section (Appendix K). A full listing of the days and the observed 24-
hour PM2.5 concentrations from the monitors in the nonattainment areas can be found in either
Appendix J or Appendix K.
Data was extracted for only the grid cells that contained portions of either of the PM2.5
nonattainment areas. Figure 6.3.1-1 highlights the 50 cells that encompass the North Carolina
PM2.5 nonattainment areas.
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The cell counts were binned based on concentration ranges of 15 μg/m3 intervals to help
illuminate the change in severity on the days in North Carolina with the highest PM2.5
concentrations. Figure 6.3.1-2 presents the cell count results both graphically and in tabular
form. The graph clearly shows a striking increase in the number of days below 15 μg/m3. By
2009, 41.57% of cells fall in the 0 –15 μg/m3 range, a substantial increase from the 17.21% in
2002. Raw cell counts show a total of 341 cells shifted to the 0 – 15 μg/m3 range between 2002
and 2009 (Table 6.3.1-1).
Figure 6.3.1-2 also shows a decrease in the number of cells in the 15 – 30 μg/m3 bin (269 cell
decrease) and the 30 - 45μg/m3 bin (75 cell decrease). The number of cells in the 45 –60 range
remain relatively constant from 2002 to 2009. A closer examination of the daily cell counts
shows that all of the cells in the highest concentration category occur on the same day in both the
2002 and 2009 modeling and are likely associated with a fire. Overall, the results from the air
quality modeling metric are encouraging. The metric shows a substantial increase in the number
of cells below 15 μg/m3, and an increase in cells below 30μg/m3.
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Modeling Year
Percentage of Nonattainment Area
341
-269
-75
3
One way to acquire modeling sensitivity runs is to examine the modeling results from other
RPOs or from USEPA modeling studies. Other modeling studies may use different physical and
chemical modeling options for their meteorological and air quality modeling runs, which would
provide a comparison or sensitivity based on these different options.
An air quality modeling exercise that contained results for North Carolina PM2.5 nonattainment
areas is the USEPA’s modeling for the CAIR. The Technical Support Document for the final
CAIR, March 2005, provided modeling results with and without the implementation for the
CAIR. Differences between the USEPA’s modeling and the attainment demonstration are: 1) the
meteorology was for 2001, 2) the DVB was the weighted design values for the 1999-2003 period
and 3) the modeling results were for 2010. The DVF was calculated using the CAIR SMAT
tool, so methodologies between the CAIR DVF and the values presented in Section 6.4 are the
same. These modeling results are listed in Table 6.3.2-1 below.
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The USEPA’s results were for the highest monitor in a county where more than one monitor is

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The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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This document contains North Carolina's attainment demonstration for the Hickory and
Greensboro/Winston-Salem/High Point fine particulate matter nonattainment areas, which
demonstrates that both of these areas will meet the National Ambient Air Quality Standards for
fine particulate matter by April 5, 2010. These areas include the entire counties of Catawba,
Davidson, and Guilford.
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INTRODUCTION
Fine particulate matter, also known as fine particles and PM2.5, refers to airborne particles less
than or equal to 2.5 micrometers (μm) in diameter. Fine particles are treated as though they are a
single pollutant, but they come from many different sources and are composed of many different
compounds. PM2.5 exposure adversely affects human health, especially respiratory and
cardiovascular systems. Individuals particularly sensitive to PM2.5 exposure include children,
people with heart and lung disease, and older adults.
A variety of meteorological and geographic factors influence the concentration levels of fine
particles, including both the regional and local distribution of urbanized areas, primary and
precursor emissions sources, and natural features such as oceans and forests. PM2.5
concentrations can also be high and exceed the national ambient air quality standards (NAAQSs)
for fine particulate matter at any time of the year. Therefore, the United States Environmental
Protection Agency (USEPA) mandates the year round monitoring of PM2.5 concentrations
throughout the country (40 CFR 58.App. D, 4.7).
NATIONAL AMBIENT AIR QUALITY STANDARD
In 1997, the USEPA promulgated the primary (health) and secondary (welfare) NAAQSs for
PM2.5 (40 CFR 50.7), setting the standard at a 15.0 micrograms per cubic meter (μg/m3) annual
average and at a 65 μg/m3 daily or 24-hour average. A violation of the annual PM2.5 NAAQS
occurs when the annual average PM2.5 concentration averaged over a three consecutive year
period is equal to or greater than 15.1 μg/m3. A violation of the daily PM2.5 NAAQS occurs
when the annual 98th percentile of daily PM2.5 concentration averaged over a three consecutive
year period is equal to or greater than 66 μg/m3. The annual or daily PM2.5 design value for a
nonattainment area is the highest design value for any monitor in that area.
The USEPA designated areas as nonattainment for the annual and daily PM2.5 NAAQSs based
upon air quality monitoring data measured during 2001, 2002 and 2003. The effective date of
nonattainment designations was April 5, 2005.
NATURE OF PROBLEM IN NORTH CAROLINA
In North Carolina, there were two areas designated as nonattainment for violating the annual
PM2.5 standard (Figure 1). All areas of North Carolina met the daily PM2.5 standard. This PM2.5
attainment demonstration submittal covers the Hickory PM2.5 nonattainment area (Catawba
County) and Greensboro/Winston-Salem/High Point PM2.5 nonattainment area (referred to as the
Triad area and consists of Davidson and Guilford Counties) with respect to the violations of the
annual PM2.5 standard.
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When the annual PM2.5 concentrations in both nonattainment areas are analyzed by the
percentages of their individual component species, the organic carbon (OC) and sulfate (SO4)
components each account for approximately one-third of the total PM2.5 mass, the ammonium
component makes up approximately ten percent of the total PM2.5 mass, and the remaining
nitrate (NO3), elemental carbon, crustal material, and particle bound water components each
contribute approximately five percent or less of the total PM2.5 mass. The percentages of species
contribution fluctuate throughout the year with the most significant changes to SO4 and NO3.
SO4 is more pronounced in the summertime or warm season months than during the wintertime.
NO3 fluctuates from almost undetectable in the summertime to as much as ten percent
contribution of the total PM2.5 mass during the coldest portion of the winter.
The speciated analysis of the PM2.5 concentrations in the Hickory and Triad PM2.5 nonattainment
areas demonstrates that the OC and SO4 components are the most important portions of the total
PM2.5 mass throughout the year. OC is predominately attributed to biogenic emissions sources.
SO4 is associated with sulfur dioxide (SO2) emissions. When evaluated across North Carolina
and also throughout both nonattainment areas and surrounding regions, the SO2 is primarily from
the point source sector. For this reason, SO2 emissions controls from point sources are believed
to be the most appropriate strategy for addressing the PM2.5 nonattainment issues for Hickory
and the Triad.
CONTROLS APPLIED
Several control measures already in place or being implemented over the next few years will
reduce stationary point, highway mobile, and non-road mobile sources emissions. The expected
Federal and State control measures were modeled for the attainment year of 2009.
The Federal control measures that were modeled included the Tier 2 vehicle standards; the
heavy-duty gasoline and diesel highway vehicle standards; low sulfur gasoline and diesel fuels,
large non-road diesel engines standards; the non-road spark-ignition engines and recreational
engines standard; and the Clean Air Interstate Rule (CAIR). Due to the Court challenges of
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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CAIR in 2008, the USEPA will be making changes to this program soon. However, the existing
CAIR rules will remain in place until the USEPA promulgates changes to the program.
The State control measures that were modeled included the Clean Air Bill, in which the vehicle
emissions inspection and maintenance program was expanded from 9 counties to 48; the NOx
SIP Call Rule, CAIR, and the Clean Smokestacks Act, which will significantly reduce SO2
emissions from the large electrical generation units with implementation beginning prior to the
2009 attainment year and well in advance of the Federal Clean Air Interstate Rule. The Clean
Smokestacks Act further requires the coal-fired power plants to meet an annual SO2 emissions
cap without an option of emissions trading from outside of North Carolina.
ATTAINMENT TEST RESULTS
A modeled attainment test was applied to the air quality modeling results to determine if the
annual PM2.5 NAAQS will be met by the attainment year 2009. The baseline period for the air
quality modeling was centered on 2002 or the midpoint of the three years used for nonattainment
designations.
For all FRM sites in the Hickory and Triad PM2.5 nonattainment areas, the future annual PM2.5
concentrations derived from the modeled attainment test were less than 15.0 μg/m3 (Table 1).
Therefore, the modeling assessment indicated that both nonattainment areas will attain the
annual PM2.5 NAAQS by 2009.
County FRM Monitoring
Site
2001-2003, Current
Design Value
(μg/m3)
2009, Future Year
Predicted Design
Value
(μg/m3)
The North Carolina Division of Air Quality (NCDAQ) provided a strong set of supplemental
analyses further supporting that the Hickory and Triad PM2.5 nonattainment areas will attain the
annual PM2.5 NAAQS by April 5, 2010. These analyses included evaluating the air quality
modeling from an absolute percentage reduction perspective compared to the annual PM2.5
NAAQS, investigating current air quality data trends along with the emission reductions that
have recently occurred, and considering air quality modeling results from other region and
national modeling exercises.
The NCDAQ believes that the modeling attainment demonstration, in conjunction with the
supplemental analyses, provides the necessary evidence that the Hickory and Triad PM2.5
nonattainment areas will attain the annual PM2.5 NAAQS by the April 5, 2010 attainment date
and furthermore continue to maintain the daily PM2.5 NAAQS. In fact, both nonattainment areas
have already attained the 1997 annual PM2.5 standard with the 2006-2008 ambient air quality
data, one year earlier than required.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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1.1 What is fine particulate matter? ............................................................................................ 1
1.2 What is the National Ambient Air Quality Standard? .......................................................... 2
1.3 Nature of Problem in North Carolina ................................................................................... 2
1.4 Major Contributors to PM2.5 in the North Carolina Nonattainment Areas ........................... 5
1.5 Clean Air Act Requirements ................................................................................................. 6
2.1 PM2.5 Component Species Analysis ..................................................................................... 7
2.2 Attribution of Emissions Sources ......................................................................................... 9
2.3 Clean Air Fine Particulate Implementation Rule Presumptions on Precursor Pollutants ... 12
3.1 Analysis Method ................................................................................................................. 13
3.2 Model Selection .................................................................................................................. 14
3.2.1 Selection of Air Quality Model .................................................................................... 14
3.2.2 Selection of Meteorological Model ............................................................................. 15
3.2.3 Selection of Emissions Processing System .................................................................. 18
3.3 Selection of the Modeling Year .......................................................................................... 19
3.4 Modeling Domains ............................................................................................................. 20
3.4.1 Horizontal Modeling Domain ...................................................................................... 20
3.4.2 Vertical Modeling Domain .......................................................................................... 22
3.5 Baseline Emissions Inventory ............................................................................................. 23
3.5.1 Stationary Point Sources .............................................................................................. 26
3.5.2 Stationary Area Sources ............................................................................................... 27
3.5.3 Off-Road Mobile Sources ............................................................................................ 28
3.5.4 Highway Mobile Sources ............................................................................................. 28
3.5.5 Biogenic Emission Sources .......................................................................................... 29
4.1 Meteorological Model Performance ................................................................................... 30
4.2 Air Quality Model Performance ......................................................................................... 31
4.2.1 Modeling Performance Goals, and Criteria ................................................................. 32
4.2.2 Domain-Wide Model Performance .............................................................................. 33
4.2.3 Nonattainment Area Model Performance .................................................................... 35
4.2.4 Air Quality Model Performance Summary .................................................................. 37
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5.1 Federal Control Measures ................................................................................................... 39
5.1.1 Tier 2 Vehicle Standards .............................................................................................. 39
5.1.2 Heavy-Duty Gasoline and Diesel Highway Vehicles Standards ................................. 39
5.1.3 Large Non-road Diesel Engines Rule .......................................................................... 39
5.1.4 Non-road Spark-Ignition Engines and Recreational Engines Standard ....................... 40
5.1.5 NOx SIP Call in Surrounding States ............................................................................ 40
5.1.6 Clean Air Interstate Rule.............................................................................................. 40
5.2 State Control Measures ....................................................................................................... 41
5.2.1 Clean Air Bill ............................................................................................................... 41
5.2.2 NOx SIP Call Rule ....................................................................................................... 42
5.2.3 Clean Smokestacks Act ................................................................................................ 42
5.2.4 Open Burning Bans ...................................................................................................... 43
5.2.5 Clean Air Interstate Rule.............................................................................................. 43
6.1 Attainment Test Introduction .............................................................................................. 44
6.2 Attainment Test Results ...................................................................................................... 45
6.3 Supplemental Analyses ....................................................................................................... 45
6.3.1 Air Quality Modeling Metrics ..................................................................................... 46
6.3.2 Other Modeling Results ............................................................................................... 48
6.3.3 Air Quality Trends and Additional Reductions in Emissions ...................................... 49
6.4 Unmonitored Area Analysis ............................................................................................... 51
6.5 Data Access ......................................................................................................................... 53
7.1 Reasonable Available Control Measures ............................................................................ 54
7.2 Reasonable Further Progress .............................................................................................. 55
7.3 Actual Emissions Inventory ................................................................................................ 55
7.4 Periodic Emissions Inventory ............................................................................................. 55
7.5 Permit Program Requirements ............................................................................................ 55
7.6 Other Measures ................................................................................................................... 56
7.7 Compliance with Section 110(a)(2) .................................................................................... 56
7.8 Equivalent Techniques ........................................................................................................ 56
7.9 Contingency Measures ........................................................................................................ 56
8.1 Transportation Conformity ................................................................................................. 58
8.2 Pollutants to be Considered ................................................................................................ 58
8.3 Highway Mobile Source Direct PM2.5 Emissions ............................................................. 59
8.4 Establishing PM2.5 and NOx Motor Vehicle Emission Budgets ......................................... 61
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Figure 1. Annual PM2.5 Nonattainment Boundaries for North Carolina ....................................... iv
Figure 1.3-1. Annual PM2.5 Nonattainment Boundaries for North Carolina .................................. 3
Figure 1.3-2. PM2.5 Monitoring Sites In North Carolina ................................................................ 3
Figure 1.3-3. PM2.5 FRM Monitoring Sites in the Hickory and Triad Nonattainment Areas ....... 4
Figure 1.3-4. North Carolina PM2.5 Speciation for 2004 ................................................................ 5
Figure 2.1-1. 2002 PM2.5 Speciated Mass Contribution at Hickory Using SANDWICH .............. 8
Figure 2.1-2. 2002 PM2.5 Speciated Mass Contribution at Lexington Using SANDWICH ........... 8
Figure 2.1-3. 2002 PM2.5 Speciated Mass Contribution at Mendenhall Using SANDWICH ........ 9
Figure 2.2-1. Hickory PM2.5 Nonattainment Area SO2 Emissions in 2002 .................................. 10
Figure 2.2-2. Triad PM2.5 Nonattainment Area SO2 Emissions in 2002 ...................................... 11
Figure 2.2-3. North Carolina Total SO2 Emissions in 2002 ......................................................... 11
Figure 3.4.1-1. The MM5 and CMAQ_SOA 36-km Horizontal Domains ................................... 21
Figure 3.4.1-2. VISTAS 12-km Modeling Domain ...................................................................... 21
Figure 4.2.2-1. VISTAS STN Soccer Plots .................................................................................. 33
Figure 4.2.2-2. VISTAS STN Bugle Plots .................................................................................... 34
Figure 4.2.2-3. VISTAS FRM Soccer Plots ................................................................................. 34
Figure 4.2.2-4. VISTAS FRM Bugle Plots ................................................................................... 35
Figure 4.2.3-2. Hickory STN Bugle Plots..................................................................................... 36
Figure 4.2.3-3. Hickory FRM Soccer Plots .................................................................................. 37
Figure 4.2.3-4. Hickory FRM Bugle Plots .................................................................................... 37
Figure 5.2.1-1. North Carolina’s OBDII Test Phase-in Map ........................................................ 41
Figure 6.3.1-1. Area for which the Air Quality Metrics were Applied ........................................ 47
Figure 6.3.1-2. Percentage of Cell in PM2.5 Nonattainment Areas within Concentration
Categories for 2002 and 2009. Table of Actual Values is Presented on the Right. ..................... 48
Figure 6.3.3-1. Annual PM2.5 Average Concentrations for the FRM Monitors in the Hickory and
Triad Nonattainment Areas ........................................................................................................... 50
Figure 6.3.3-2. Annual SO2 Emissions From EGUs In NC .......................................................... 51
Figure 6.4-1. PM2.5 Monitors and Nonattainment Areas with Respect to the VISTAS 12km Grid
Domain ......................................................................................................................................... 52
Figure 6.4-2. PM2.5 Monitors and 2009 Modeled Attainment Spatial Field ................................. 53
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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Table 1. Current And Future Year Predicted Annual PM2.5 Concentrations .................................. v
Table 1.3-1. PM2.5 Concentrations and Design Values for the FRM monitors in the Hickory and
Triad PM2.5 Nonattainment Areas ................................................................................................... 4
Table 3.4.1-1: Vertical Layer Definition For MM5 and CMAQ .................................................. 23
Table 3.5-1. 2002 Annual Emission Summaries .......................................................................... 25
Table 4.2.1-1. Established Model Performance Goals and Criteria for the PM2.5 Component
Species ......................................................................................................................................... 32
Table 5.2.1-1 OBDII Phase-in Effective Dates ............................................................................ 42
Table 6.2-1. Quarterly Mean and Annual Mean PM2.5 Mass Estimates for 2009 ........................ 45
Table 6.3.1-1. Number of Cells within Concentration Bins. Increases (decreases) in the Number
of Cells within the Bins are Noted by Red (Blue) Coloration in the Last Column. ..................... 48
Table 6.3.2-1. USEPA’s CAIR Modeling Results ........................................................................ 49
Table 6.3.3-1. Annual Average PM2.5 Concentrations for the Past 10 Years ............................... 49
Table 6.3.3-2. Three Year Design Values for the FRM Monitors in the Hickory and Triad PM2.5
Nonattainment Areas .................................................................................................................... 50
Table 8.4-1. County Level PM2.5 Highway Mobile Emissions for 2009 ...................................... 61
Table 8.4-2. County Level NOX Highway Mobile Emissions for 2009 ....................................... 61
Table 8.4-3. County Level PM2.5 MVEBs for 2009 ..................................................................... 62
Table 8.4-4. County Level NOx MVEBs for 2009 ....................................................................... 62
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Appendix A: Policy and Memorandums
Appendix B: Stakeholders Correspondence Regarding Motor Vehicle Emissions Budgets
Appendix C: Air Quality Data
Appendix D: Modeling Protocol
Appendix E: Emissions Inventory Summary
Appendix F: Emissions Inventory Documentation
Appendix G: Emissions Inventory Quality Assurance Project Plan
Appendix H: Emissions Modeling and Related
Appendix I: Meteorological Development Documentation
Appendix J: Model Performance Evaluation
Appendix K: Modeling Results
Appendix L: Attainment Test
Appendix M: Adopted State Measures
Appendix N: Contingency Measures Documentation
Appendix O: Insignificance of NH3 and VOCs to PM2.5 Attainment in North Carolina
Appendix P: Supporting Documentation from VISTAS and ASIP
Appendix Q: Public Notice Report, Comments Received, and Responses
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Fine particulate matter, also known as fine particles and PM2.5, refers to airborne particles less
than or equal to 2.5 micrometers (μm) in diameter. Fine particles are treated as though they are a
single pollutant, but they come from many different sources and are composed of many different
compounds. PM2.5 exposure adversely affects human health, especially respiratory and
cardiovascular systems. Individuals particularly sensitive to PM2.5 exposure include children,
people with heart and lung disease, and older adults.
PM2.5 can be liquid, solid, or can have a solid core surrounded by liquid. PM2.5 can include
material produced by combustion, photochemical reactions, and can contain salt from sea spray
and soil-like particles. Particles are distinguished based on the method of formation. Primary
particles are particles directly emitted into the atmosphere and retain the same chemical
composition as when they were released. Secondary particles are those formed through chemical
reactions involving atmospheric oxygen, water vapor, hydroxyl radical, nitrates, sulfur dioxide
(SO2), oxides of nitrogen (NOx), and organic gases from natural and anthropogenic sources.
PM2.5 can therefore be composed of varying amount of different species, including:
• Sulfates
• Nitrates (usually found in the form of ammonium nitrate)
• Ammonium
• Hydrogen ion
• Particle bound water
• Elemental carbon
• Organic compounds
Primary organic species (from cooking and combustion)
Secondary organic compounds
• Crustal material (includes calcium, aluminum, silicon, magnesium, and iron)
• Sea salt (generally only found at coastal monitoring sites)
• Transitional metals
• Potassium (generally from wood burning or cooking)
The most significant sources of PM2.5 and its precursors are coal-fired power plants, industrial
boilers and other combustion sources. These emissions are often transported over large
distances. Other sources of PM2.5 emissions include mobile sources, area sources, biogenic,
fires, windblown dust, and oceans.
A variety of meteorological and geographic factors influence the concentration levels of fine
particles, including both the regional and local distribution of urbanized areas, primary and
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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precursor emissions sources, and natural features such as oceans and forests. PM2.5
concentrations can also be high and exceed the national ambient air quality standards (NAAQSs)
for fine particulate matter at any time of the year. Therefore the United States Environmental
Protection Agency (USEPA) mandates the year round monitoring of PM2.5 concentrations
throughout the country (40 CFR 58.App. D, 4.7).
In 1997, the USEPA promulgated the primary (health) and secondary (welfare) NAAQSs for
PM2.5 (40 CFR 50.7), setting the standard at a 15.0 micrograms per cubic meter (μg/m3) annual
average and at a 65 μg/m3 daily or 24-hour average. A violation of the annual PM2.5 NAAQS
occurs when the annual average PM2.5 concentration averaged over a three consecutive year
period is equal to or greater than 15.1 μg/m3. A violation of the daily PM2.5 NAAQS occurs
when the annual 98th percentile of daily PM2.5 concentration averaged over a three consecutive
year period is equal to or greater than 66 μg/m3. The annual or daily PM2.5 design value for a
nonattainment area is the highest design value for any monitor in that area.
Since the 1977 amendments to the Clean Air Act (CAA), areas of the country that violated the
ambient standard for a particular pollutant were formally designated as nonattainment for that
pollutant. This formal designation concept was retained in the 1990 Amendments (CAAA).
With the implementation of the PM2.5 standard, areas could be designated under Section 172 of
the CAAA (subpart 1) and have five years from designation to attain the standard.
The USEPA designated areas as nonattainment for the annual and daily PM2.5 NAAQSs based
upon air quality monitoring data measured during 2001, 2002 and 2003. The effective date of
nonattainment designations was April 5, 2005.
In North Carolina, there were two areas designated as nonattainment for violating the annual
PM2.5 standard (Figure 1.3-1). All areas of North Carolina met the daily PM2.5 standard. This
PM2.5 attainment demonstration submittal covers the Hickory PM2.5 nonattainment area
(Catawba County) and Greensboro/Winston-Salem/High Point PM2.5 nonattainment area
(referred to as the Triad area and consists of Davidson and Guilford Counties) with respect to the
violations of the annual PM2.5 standard.
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Figure 1.3-2 displays the distribution of the PM2.5 monitoring sites across North Carolina. A
closer view of the PM2.5 federal reference method (FRM) monitoring sites in the Hickory and
Triad PM2.5 nonattainment areas is found in Figure 1.3-3. The Hickory monitoring site is the
only FRM monitor in the Hickory PM2.5 nonattainment area. There are two FRM monitoring
sites, Lexington and Mendenhall, in the Triad PM2.5 nonattainment area.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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Table 1.3-1 contains the quarterly and annual average PM2.5 concentrations for the FRM
monitors in the PM2.5 nonattainment areas for the three-year period used in the nonattainment
designation determinations. Table 1.3-1 also presents the 2001-2003 PM2.5 design value for the
FRM monitors based on these quarterly and annual averages. The historic quarterly, yearly, and
design value air quality data for the FRM monitors in both PM2.5 nonattainment areas can be
found in Appendix C.
County FRM Monitoring
Site Year
1st
Quarter
(Q1)
2nd
Quarter
(Q2)
3rd
Quarter
(Q3)
4th
Quarter
(Q4)
Annual
Average
Design
Value
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As mentioned in Section 1.1, PM2.5 is composed of many species from varying sources.
Figure 1.3-4 presents the North Carolina statewide averaged PM2.5 speciation data from the
speciation trends network (STN) monitors for the year 2004. The figure presents sulfates (SO4)
and organic carbons (OC) as the main contributors to PM2.5, each with 29%; ammonium (NH4)
contributes 11%; nitrates (NO3) contribute 7%; elemental carbon (EC) is approximately 4%; and
crustal material is 3% of the total PM2.5 mass. The “other” portion of the PM2.5 that accounts for
17% of the mass can be attributed to water (H2O), sea salts, and other trace materials captured
with the STN monitors.
When the annual PM2.5 concentrations in both nonattainment areas are analyzed by the
percentages of their individual component species, a similar distribution of components are
found. The OC and SO4 components each account for approximately one-third of the total PM2.5
mass; NH4 makes up approximately ten percent of the total PM2.5 mass; and the remaining NO3,
EC, crustal material, and particle bound water components each contribute approximately five
percent or less of the total PM2.5 mass. Individual plots of the speciated PM2.5 data (similar to
Figure 1.3-4) from the three PM2.5 monitoring locations in the nonattainment areas can be found
in Appendix C.
The percentages of species contribution fluctuate throughout the year with the most significant
changes to SO4 and NO3. SO4 is more pronounced in the summertime or warm season months
than during the wintertime. NO3 fluctuates from almost undetectable in the summertime to as
much as ten percent contribution of the total PM2.5 mass during the coldest portion of the winter.
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The speciated analysis of the PM2.5 concentrations in the Hickory and Triad PM2.5 nonattainment
areas demonstrates that the OC and SO4 components are the most important portions of the total
PM2.5 mass throughout the year. OC is predominately attributed to biogenic emissions sources.
SO4 is associated with SO2 emissions. When evaluated across North Carolina and also
throughout both nonattainment areas and surrounding regions, the SO2 is primarily from the
point source sector. For this reason, SO2 emissions controls from point sources are believed to
be the most appropriate strategy for addressing the current PM2.5 nonattainment issues for
Hickory and the Triad.
Further details on the nature of the PM2.5 problem in both PM2.5 nonattainment areas are
discussed in Section 2 and can also be found in the Conceptual Description of Fine Particulate
Matter in North Carolina section of Appendix D.1.
Section 172(c) as amended, contains the general requirements for nonattainment areas. These
requirements are listed below and are discussed in more detail in Section 7.
Section 172(c) Nonattainment Plan Provisions
(1) Reasonable available control measures (RACM)
(2) Reasonable further progress (RFP)
(3) Actual emissions inventory and periodic emissions inventory
(4) New source review (NSR)
(5) Permit requirements for new and modified sources
(6) Other measures as may be necessary to provide attainment by specified
attainment date
(7) Compliance with Section 110(a)(2)
(8) Equivalent techniques
(9) Contingency measures
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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As suggested in the Section 1.4, SO2 emissions are believed to be the most appropriate strategy
for addressing the 1997 PM2.5 NAAQS for the Hickory and Triad nonattainment areas. This
finding is based on several factors including:
• An analysis of the percentage contribution of the PM2.5 component species annually
and seasonally within the nonattainment areas
• Attribution of emissions sources to these PM2.5 component species
• Clean Air Fine Particulate Implementation Rule presumptions on precursor pollutants
To fully understand the nature of the PM2.5 nonattainment issues in the Hickory and Triad
nonattainment areas, it is important to analyze the percentage contribution of the individual
PM2.5 component species, both from an annual perspective and seasonally throughout the year.
Unfortunately, the FRM monitoring sites only provide a total mass PM2.5 concentration and do
not provide any information concerning the speciated breakdown of various components. A
separate PM2.5 monitoring network, STN, does allow for the speciation of these components, but
the STN PM2.5 concentration data is not directly comparable to the FRM PM2.5 concentration
data due to slight difference in the monitoring methodology. This creates an issue in using raw
STN PM2.5 data in an attainment demonstration, because it is not absolutely equivalent to the
FRM PM2.5 data of which the nonattainment is based and of which attainment will ultimately be
evaluated.
To address this issue, Neil Frank with the USEPA developed an approach to use the raw STN
PM2.5 data to appropriately estimate the components of PM2.5 as measured by the FRM monitors.
The approach is termed the “ ulfate, djusted itrate, erived ater, nferred arbonaceous
material balance approac ” method or SANDWICH (Frank, 2006). The SANDWICH approach
is discussed in greater detail in Appendix L.
Using the SANDWICH approach, it is now possible to analyze the percentage contribution of the
individual PM2.5 component species relative to the total FRM PM2.5 mass. Figures 2.1-1 through
2.1-3 present the speciated mass contributions of the component species at the Hickory,
Lexington, and Mendenhall monitoring sites, respectively. The speciated mass contributions
displayed are for the 2002 baseline year. Figures 2.1-1 through 2.1-3 illustrate daily speciated
mass contributions for each day of the 2002 calendar year (expressed in Julian days) from left to
right, with the farthest right bar of the charts representing the 2002 annual averaged speciated
mass contributions.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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Julian Day
Mass (μg m-3)
Julian Day
Mass (μg m-3)
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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Julian Day
Mass (μg m-3)
From each of the three 2002 PM2.5 speciated mass contribution plots, it is clear that SO4 and OC
are the dominant PM2.5 components throughout the year. SO4 is most pronounced during the
summertime, but remains a reasonably important component of the total PM2.5 mass in any of the
seasons. NH4 and H2O are less dominant than SO4 and OC but are relatively consistent in each
season. EC and crustal material are much less prevalent at any time of the year. Finally, NO3
contributions are almost undetectable in the summertime to as much as ten percent contribution
of the total PM2.5 mass during the wintertime.
Precursor pollutants to PM2.5 can be emitted directly, such as in smoke from a fire, or they can
form from chemical reactions of gases such as SO2, nitrogen dioxide and some organic gases.
Sources of these precursor pollutants include power plants, gasoline and diesel engines, wood
combustion, and high-temperature industrial processes such as smelters and steel mills. Other
sources of the PM2.5 precursor pollutants include mobile sources, area sources, biogenic, fires,
windblown dust, and oceans.
The speciated analysis of the PM2.5 concentrations in the Hickory and Triad PM2.5 nonattainment
areas presented above demonstrates that the OC and SO4 components are the most important
portions of the total PM2.5 mass throughout the year at all three monitoring locations. OC is
predominately attributed to biogenic volatile organic compound (VOC) emissions. SO4 is
associated with SO2 emissions. NH4 can have a variety of sources including both industrial and
natural processes. What little NO3 is present in the PM2.5 nonattainment areas throughout the
year are attributed to NOx from combustion sources. Of all these components and associated
emission sources, SO4 is the only dominant PM2.5 component species found throughout the year
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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that is attributed to a set of emissions source (SO2) that are controllable through regulatory
actions by the North Carolina Division of Air Quality (NCDAQ).
When evaluated throughout both nonattainment areas and across North Carolina, SO2 is
primarily from the point source sector. Figures 2.2-1 and 2.2-2 present the SO2 emissions from
the various source sectors in the Hickory and Triad PM2.5 nonattainment areas, respectively.
Both figures are presented on same vertical axis scales to illustrate the significance of a single
point source facility that is inside of the Hickory nonattainment area and immediately adjacent
and upwind of the Triad nonattainment area. The magnitude of the point source sector
completely masks the SO2 emissions from all other source categories. When SO2 emissions by
source category are evaluated across North Carolina (Figure 2.2-3), the point source emissions
are 6 times larger than emissions contained in either of the nonattainment area SO2 emissions
plots. Again, the magnitude of the point source sector completely masks the SO2 emissions from
all other source sectors.
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The USEPA’s Clean Air Fine Particulate Implementation Rule (72 FR 20586), commonly
referred to as the PM2.5 Implementation Rule, guides States as they develop state implementation
plans in response to annual and/or daily PM2.5 nonattainment. It establishes a hierarchy of
precursor pollutants: SO2 is always considered a precursor, NOx is presumptively a precursor,
and VOCs and ammonia are presumed not to be precursors. The State of North Carolina is
following the assertions and presumptions of significant and insignificant precursor pollutants
established in the PM2.5 Implementation Rule in this attainment demonstration. Further
discussion on the significant or insignificance of the various precursor pollutants is discussed in
Appendix O.
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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The attainment modeling for the Hickory and Triad PM2.5 nonattainment areas was performed in
conjunction with the regional haze modeling being done by Southeast Regional Planning
Organization (RPO), Visibility Improvement State and Tribal Association of the Southeast
(VISTAS) and the PM2.5 and ozone (O3) modeling being done by the Association of
Southeastern Integrated Planning (ASIP). VISTAS and ASIP are managed by the ten Southeast
states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina,
Tennessee, Virginia and West Virginia). Since the VISTAS/ASIP regional modeling utilized
annual simulations and includes modeling for the attainment year required for the Hickory and
Triad PM2.5 nonattainment areas, the NCDAQ decided to use this modeling for its attainment
demonstration. The sections below outline the methods and inputs used by VISTAS/ASIP for
the regional modeling.
The modeling analysis is a complex technical evaluation that begins by selection of the modeling
system. VISTAS decided to use the following modeling system:
• Meteorological Model: The Pennsylvania State University/National Center for
Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5) is a
nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale
photochemical, fine particulate matter, and regional haze regulatory modeling
studies.
• Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling
system is an emissions modeling system that generates hourly gridded speciated emission
inputs of mobile, non-road mobile, area, point, fire and biogenic emission sources for
photochemical grid models.
• Air Quality Model: The USEPA’s Models-3/ Community Multiscale Air Quality
(CMAQ) modeling system is a “One-Atmosphere” photochemical grid model capable of
addressing O3, particulate matter, visibility and acid deposition at regional scale for
periods up to one year.
Additionally, an historical year is selected to model that represents typical meteorological
conditions in the Southeast when high ozone, high PM2.5 and poor visibility are observed
throughout the region. Once the historical year is selected, meteorological inputs are developed
using the meteorological model. Emission inventories are also developed for the historical year
and processed through the emissions model. These inputs are used in the air quality model to
predict ozone, PM2.5 and visibility, with the results compared to the historic data. The model
performance is evaluated by comparing the modeled predicted data to the historic air quality
data.
Once model performance is deemed adequate, typical baseline and future year emissions are
processed through the emissions model. For this demonstration, the baseline year was 2002,
which corresponds with the same year as the historic meteorology used in the modeling. The
attainment future year the NCDAQ is using for this demonstration is 2009, since the mandatory
attainment date for the Hickory and Triad PM2.5 nonattainment areas is April 5, 2010. The
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attainment date is set prior to the completion of the 2010 calendar year; therefore the attainment
of the NAAQS would have to be met by the end of 2009. These emissions are processed through
the air quality model with the meteorological inputs. The air quality modeling results are used to
determine a relative reduction in future PM2.5 concentrations, which is used in the attainment
demonstration.
The complete modeling protocol used by the NCDAQ for this analysis can be found in
Appendix D.1. For additional reference, the VISTAS/ASIP modeling protocol can be found in
Appendix D.2.
To ensure that a modeling study is defensible, care must be taken in the selection of the models
to be used. The models selected must be scientifically appropriate for the intended application
and be freely accessible to all stakeholders. Scientifically appropriate means that the models
address important physical and chemical phenomena in sufficient detail, using peer-reviewed
methods. Freely accessible means that model formulations and coding are freely available for
review and that the models are available to stakeholders, and their consultants, for execution and
verification at little or no cost.
The following sections outline the criteria for selecting a modeling system that is both defensible
and capable of meeting the study's goals. These criteria were used in selecting the modeling
system used for this modeling attainment demonstration.
For an air quality model to qualify as a candidate for use in an attainment demonstration, a State
needs to show that it meets several general criteria:
• The model has received a scientific peer review.
• The model can be demonstrated applicable to the problem on a theoretical basis.
• Data bases needed to perform the analysis are available and adequate.
• Available past appropriate performance evaluations have shown the model is not biased
toward underestimates or overestimates.
• A protocol on methods and procedures to be followed has been established.
• The developer of the model must be willing to make the source code available to users
for free or for a reasonable cost, and the model cannot otherwise be proprietary.
The air quality model selected for this study was CMAQ version 4.5, which was the most recent
release at the point the attainment modeling exercise started. For more than a decade, the
USEPA has been developing the Models-3 CMAQ modeling system with the overarching aim of
producing a “One-Atmosphere” air quality modeling system capable of addressing ozone, fine
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particulate matter, visibility and acid deposition within a common platform. The original
justification for the Models-3 development emerged from the challenges posed by the CAAA
and the USEPA’s desire to develop an advanced modeling framework for “holistic”
environmental modeling utilizing state-of-science representations of atmospheric processes in a
high performance computing environment. The USEPA completed the initial stage of
development with Models-3 and released the CMAQ model in mid 1999 as the initial operating
science model under the Models-3 framework.
Another reason for choosing CMAQ as the atmospheric model is the ability to do one-atmospheric
modeling. Since the NCDAQ will be using the same modeling exercise for the
ozone and PM2.5 attainment demonstration state implementation plans (SIPs), as well as the
regional haze SIP, having a model that can handle both ozone and particulate matter is essential.
A number of features in CMAQ’s theoretical formulation and technical implementation make the
model well suited for annual PM2.5 modeling.
CMAQ contains three options for treating secondary organic aerosol (SOA), latest being the
Secondary Organic Aerosol Model (SORGAM) that was updated in August 2003 to be a
reversible semi-volatile scheme whereby VOC emissions can be converted to condensable gases
that can then form SOA and then evaporate back into condensable gases depending on
atmospheric conditions.
The CMAQ chemical-transport model processor (CTM) requires the following inputs:
• Three-dimensional hourly meteorological fields that will be generated by the CMAQ
MCIP2.3 processing of the BAMS MM5 output
• Three-dimensional hourly emissions generated by SMOKE
• Initial conditions and boundary conditions
• Topographic information
• Land use categories
• Photolysis rates generated by the CMAQ JPROC processor
The configuration used for this modeling demonstration, as well as a more detailed description of
the CMAQ_SOA (CMAQ version with SOA modification) model, can be found in Appendix
D.1. The resulting model performance evaluation can be found in Appendix J.
Meteorological models, either through objective, diagnostic, or prognostic analysis, extend
available information about the state of the atmosphere to the grid upon which photochemical
grid modeling is to be carried out. The criteria for selecting a meteorological model are based on
both the models ability to accurately replicate important meteorological phenomena in the region
of study, and the model's ability to interface with the rest of the modeling systems, particularly
the air quality model. With these issues in mind, the following criteria were established for the
meteorological model to be used in this study:
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• Non-Hydrostatic Formulation
• Reasonably current, peer reviewed formulation
• Simulates Cloud Physics
• Publicly available at no or low cost
• Output available in I/O API format
• Supports Four Dimensional Data Assimilation
• Enhanced treatment of Planetary Boundary Layer heights for AQ modeling
The non-hydrostatic MM5 model is a three-dimensional, limited-area, primitive equation,
prognostic model that has been used widely in regional air quality model applications. The basic
model has been under continuous development, improvement, testing, and open peer-review for
more than 20 years and has been used worldwide by hundreds of scientists for a variety of
mesoscale studies.
MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical coordinate
similar to that used in many operational and research models. In the non-hydrostatic MM5, the
sigma levels are defined according to the initial hydrostatically balanced reference state so that
the sigma levels are also time-invariant. The gridded meteorological fields produced by MM5
are directly compatible with the input requirements of “one atmosphere” air-quality models using
this coordinate. MM5 fields can be easily used in other regional air quality models with different
coordinate systems by performing a vertical interpolation, followed by a mass-conservation
readjustment.
Distinct planetary boundary layer parameterizations are available for air-quality applications,
both of which represent sub-grid-scale turbulent fluxes of heat, moisture and momentum. One
scheme uses a first-order eddy diffusivity formulation for stable and neutral environments and a
modified first-order scheme for unstable regimes. The other scheme uses a prognostic equation
for the second-order turbulent kinetic energy, while diagnosing the other key boundary layer
terms.
Initial and lateral boundary conditions are specified for real-data cases from mesoscale three-dimensional
analyses performed at 12-hour intervals on the outermost grid mesh selected by the
user. Surface fields are analyzed at three-hour intervals. The GEOS-CHEM global chemical
transport model was run for 2002 to develop the initial and boundary conditions. More details
on the GEOS-CHEM model used in this attainment demonstration can be found in Appendix P .
A Cressman-based technique is used to analyze standard surface and radiosonde observations,
using the National Meteorological Center's spectral analysis, as a first guess. The lateral
boundary data are introduced using a relaxation technique applied in the outermost five rows and
columns of the coarsest grid domain.
Results of detailed performance evaluations of the MM5 modeling system in regulatory air
quality application studies have been widely reported in the literature (e.g., Emery et al., 1999;
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Tesche et al., 2000, 2003) and many have involved comparisons with other prognostic models
such as the Regional Atmospheric Modeling System (RAMS) and the Systems Application
International Mesoscale Model. The MM5 enjoys a far richer application history in regulatory
modeling studies compared with RAMS or other models. Furthermore, in evaluations of these
models in over 60 recent regional scale air quality application studies since 1995, it has generally
been found that the MM5 model tends to produce somewhat better photochemical model inputs
than alternative models.
The databases required for setting up, exercising, and evaluating the MM5 model for the 2002
season consist of various fixed and variable inputs.
• Topography: High resolution (e.g., 30 sec to 5 min) topographic information derived
from the Geophysical Data Center global datasets from the NCAR terrain databases are
available for prescribing terrain elevations throughout the 36-km and 12-km grid domain.
• Vegetation Type and Land Use: Vegetation type and land use information on the 36-km
grid may be developed using the PSU/NCAR 10 min. (~18.5 km) databases while for the
12-km grids, the United States Geological Survey (USGS) data are available.
• Atmospheric Data: Initial and boundary conditions to the MM5 may be developed from
operationally analyzed fields derived from the National Centers for Environmental
Prediction (NCEP) Eta model (40 km resolution) following the procedures outlined by
Stauffer and Seaman (1990). These 3-hour synoptic-scale initialization data include the
horizontal wind components (u and v), temperature, and relative humidity at the standard
pressure levels, plus sea-level pressure and ground temperature. Here, ground
temperature represents surface temperature over land and sea-surface temperature over
water.
• Water Temperature: Water temperatures required on both 36-km and 12-km grids can be
derived from the Eta skin temperature variable. These temperatures are bi-linearly
interpolated to each model domain and, where necessary, filtered to smooth out
irregularities.
• Clouds and Precipitation: While the non-hydrostatic MM5 treats cloud formation and
precipitation directly through explicit, resolved-scale, and parameterized sub-grid scale
processes, the model does not require precipitation or cloud input. The potential for
precipitation and cloud formation enters through the thermodynamic and cloud processes
formulations in the model. The only precipitation-related input required is the initial
mixing ratio field that is developed from the National Weather Service (NWS) and
National Meteorological Center (NMC) datasets.
• Multi-Scale Four Dimensional Data Assimilation (FDDA): The standard "multi-scale"
data assimilation strategy to be used on the 36-km and 12-km grids will objectively
analyze three-dimensional fields produced every 3 hours from the NWS rawinsonde
wind, temperature, and mixing ratio data, and similar analyses are generated every three
hours from the available NWS surface data.
The configuration used for this modeling demonstration, as well as a more detailed description of
the MM5 model, can be found in Appendix I as well as Section 4.6 of the Modeling Protocol
(Appendix D.1).
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The principal criterion for an emissions processing system is that it accurately prepares
emissions files in a format suitable for the photochemical grid model being used. The following
list includes clarification of this criterion and additional desirable criteria for effective use of the
system.
• File System Compatibility with the I/O API
• File Portability
• Ability to grid emissions on a Lambert Conformal projection
• Report Capability
• Graphical Analysis Capability
• MOBILE6 Mobile Source Emissions
• Biogenic Emissions Inventory System version 2 (BEIS-3)
• Ability to process emissions for the proposed domain in a reasonable amount of time.
• Ability to process control strategies
• Little or no cost for acquisition and maintenance
• Expandable to support other species and mechanisms
The Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System was originally
developed at the Micro-computing Center of North Carolina. As with most emissions models,
SMOKE is principally an and not a true
in which emissions estimates are simulated from “first principles”. This means that, with the
exception of mobile and biogenic sources, its purpose is to provide an efficient, modern tool for
converting emissions inventory data into the formatted emission files required by an air quality
simulation model. For mobile sources, SMOKE actually simulates emissions rates based on
input mobile-source activity data, emission factors and outputs from transportation travel-demand
models.
SMOKE was originally designed to allow emissions data processing methods to utilize emergent
high-performance-computing as applied to sparse-matrix algorithms. Indeed, SMOKE is the
fastest emissions processing tool currently available to the air quality modeling community. The
sparse matrix approach utilized throughout SMOKE permits both rapid and flexible processing
of emissions data. The processing is rapid because SMOKE utilizes a series of matrix
calculations instead of less efficient algorithms used in previous systems. The processing is
flexible because the processing steps of temporal projection, controls, chemical speciation,
temporal allocation, and spatial allocation have been separated into independent operations
wherever possible. The results from these steps are merged together at a final stage of
processing.
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SMOKE contains a number of major features that make it an attractive component of the
modeling system. The model supports a variety of input formats from other emissions
processing systems and models. It supports both gridded and county total land use scheme for
biogenic emissions modeling. SMOKE can accommodate emissions files from up to 10
countries and any pollutant can be processed by the system.
For additional information about the SMOKE model please refer to Appendix D.1.
A crucial step to SIP modeling is the selection of the period of time to model to represent current
air quality conditions and to project changes in air quality in response to changes in emissions.
The year 2002 was selected as the base year for several reasons.
The USEPA’s April 2007
(Attainment Modeling
Guidance) identifies specific goals to consider when selecting one or more episodes for use in
modeling to demonstrate the attainment of the NAAQS. The USEPA recommends that episode
selection derive from three principal criteria:
• Simulate a variety of meteorological conditions
• Model time periods in which observed concentrations are close to the appropriate
baseline design value
• Model periods for which extensive air quality/meteorological data bases exist
• Model a sufficient number of days so that the modeled attainment test applied at each
monitor violating the NAAQS is based on multiple days
VISTAS adopted a logical, stepwise approach in implementing the Attainment Modeling
Guidance in order to identify the most preferable, representative modeling year. These steps
include the following:
• Representativeness of Meteorological Conditions: The VISTAS meteorological
contractor (BAMS) identified important meteorological characteristics and data sets in
the VISTAS region directly relevant to the evaluation of candidate annual modeling
episodes. This analysis is discussed in more detail in the project report in Appendix I,
Attachment 1.
• Initial Episode Typing: At the time of selection in 2003, meteorological and air quality
data were available for 2002 for model inputs and model performance evaluation.
VISTAS used Classification and Regression Tree (CART) analyses to evaluate the
candidate modeling years (Douglas et al., 2006). The year 2002 was found to be
representative of conditions in the other years. Subsequently, these analyses were
repeated with the meteorological and air quality monitoring data for 2000 to 2004 to
evaluate how well the 2002 modeling year represented the full 2000-2004 baseline
period. This analysis confirmed that PM2.5 concentrations in 2002 were representative of
the five-year baseline period. The CART analysis is discussed in more detail in
Appendix P.
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• Data Availability: In parallel with the CART analysis, episode characterization analyses,
collaborative investigations by VISTAS states (e.g., North Carolina, Georgia, and
Florida) intensively studied the availability of PM2.5, meteorological, and emissions data
and representativeness of alternative baseline modeling periods from a regulatory
standpoint. Additionally, 2002 was the year that the USEPA was requiring states to
provide emissions inventory data for the Consolidated Emissions Reporting Rule
(CERR), it made sense to use 2002 as the modeling year to take advantage of the 2002
inventory.
• Years to be used by other RPOs: VISTAS also considered what years other RPO would
be modeling, and several had already chosen calendar year 2002 as the modeling year.
After a lengthy process of integrated studies, the episode selection process culminated in the
selection of calendar year 2002 (1 January through 31 December) as the most current,
representative, and pragmatic choice for modeling. All of the USEPA criteria for model year
selection were directly considered in this process together with many other considerations (e.g.,
timing of new emissions or aerometric data deliveries by the USEPA or the states to the
modeling teams).
The CMAQ_SOA model was run in one-way nested grid mode. This allowed the larger outer
domains to feed concentration data to the inner nested domain. One-way nesting is believed to
be appropriate for the generally stagnant conditions experienced during North Carolina’s poor air
quality episodes. Two-way nesting was not considered due to numerical and computational
uncertainty associated with the technique.
The horizontal coarse grid modeling domain boundaries were determined through a national
effort to develop a common grid projection and boundary. A smaller 12-km grid, modeling
domain was selected in an attempt to balance location of areas of interest, such as ozone and fine
particulate matter nonattainment areas. Processing time was also a factor in choosing a smaller
12-km grid, modeling domain.
The coarse 36-km horizontal grid domain covers the continental United States. This domain was
used as the outer grid domain for MM5 modeling with the CMAQ_SOA domain nested within
the MM5 domain. Figure 3.4.1-1 shows the MM5 horizontal domain as the outer most, blue grid
with the CMAQ_SOA 36-km domain nested in the MM5 domain.
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To achieve finer spatial resolution in the VISTAS states, a one-way nested high resolution (12-
km grid resolution) was used. Figure 3.4.1-2 shows the 12-km grid, modeling domain for the
VISTAS region. This is the modeling domain on which the attainment test results are based.
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The vertical grid used in the MM5 modeling primarily defines the CMAQ_SOA vertical
structure. The MM5 model employed a terrain following coordinate system defined by pressure,
using 34 layers that extend from the surface to the 100 millibars (mb). Table 3.4.1-1 lists the
layer definitions for both MM5 and for CMAQ. A layer-averaging scheme is adopted for
CMAQ to reduce the computational cost of the CMAQ simulations. A layer-averaging scheme
was used to generate 19 vertical layers for CMAQ_SOA to reduce the computational cost of the
CMAQ_SOA simulations. The effects of layer averaging were evaluated in conjunction with the
modeling effort and were found to have a relatively minor effect on the model performance
metrics when both the 34 layer and a 19 layer CMAQ_SOA models were compared to ambient
monitoring data. Further discussion on the layer-averaging scheme can be found in Section 5 of
the Modeling Protocol in Appendix D.1.
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MM5 Simulation CMAQ 19 Layers
34 0.000 100 14662 1841 19 0.000 100 14662 6536
29 0.250 325 8127 843 18 0.250 325 8127 2966
25 0.450 505 5160 607 17 0.450 505 5160 1712
22 0.600 640 3448 506 16 0.600 640 3448 986
20 0.700 730 2462 367 15 0.700 730 2462 633
18 0.770 793 1828 259 14 0.770 793 1828 428
16 0.820 838 1400 166 13 0.820 838 1400 329
14 0.860 874 1071 160 12 0.860 874 1071 160
11 0.880 892 911 158
12 0.900 910 753 78 10 0.900 910 753 155
10 0.920 928 598 77 9 0.920 928 598 153
8 0.940 946 445 76 8 0.940 946 445 76
7 0.950 955 369 75 7 0.950 955 369 75
6 0.960 964 294 74 6 0.960 964 294 74
5 0.970 973 220 74 5 0.970 973 220 74
4 0.980 982 146 37 4 0.980 982 146 37
3 0.985 986.5 109 37 3 0.985 986.5 109 37
2 0.990 991 73 36 2 0.990 991 73 36
1 0.995 995.5 36 36 1 0.995 995.5 36 36
0 1.000 1000 0 0 0 1.000 1000 0 0
The CAAA revised many of the provisions of the CAA related to attainment of the NAAQS and
the protection of visibility in mandatory Class I Federal areas (certain national parks and
wilderness areas). These revisions established new emission inventory requirements applicable
to certain areas that were designated nonattainment for certain pollutants. In the case of
particulate matter, the emission inventory provisions are in the general provisions under
Section 172(c)(3).
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There are various types of emission inventories. The first is the actual base year inventory. This
inventory is the base year emissions that correspond to the meteorological data used, which for
this modeling effort is data from 2002. These emissions are used for evaluating the air quality
model performance.
The second type of inventory is the typical base year inventory. This inventory is similar to the
actual base year inventory, except that for sources whose emissions change significantly from
year to year, a more typical emission value is used. In this modeling effort, typical emissions
were developed for the electric generating units (EGUs) and the wildland fire emissions. The air
quality modeling runs using the typical base year inventory are used to calculate relative
reduction factors used in the attainment demonstration test.
The future year base inventory is the third type of inventory and is an inventory developed for
some future year for which attainment of the fine particulate matter standard is needed. For this
modeling project, the future year inventory will be 2009, the last complete year for which the
standard must be attained. It is the future base year inventory that control strategies and
sensitivities are applied to determine what controls might be needed in order to attain and
maintain the annual PM2.5 standard.
Within each type of emission inventory, there are five different emission inventory source
classifications: stationary point and area sources, off-road and on-road mobile sources, and
biogenic sources. Stationary point sources are those sources that emit greater than a specified
tonnage per year, with data provided at the facility level. Electric generating utilities and
industrial sources are the major categories for stationary point sources.
Stationary area sources are those sources whose individual emissions are relatively small, but
due to the large number of these sources, the collective emissions from the source category could
be significant (i.e., dry cleaners, service stations, agricultural sources, fire emissions, etc.).
These types of emissions are estimated on a countywide level.
Non-road (or off-road) mobile sources are equipment that can move but do not use the roadways,
i.e., lawn mowers, construction equipment, railroad locomotives, aircraft, etc. The emissions
from these sources, like stationary area sources, are estimated on a countywide level.
On-road mobile sources are automobiles, buses, trucks, and motorcycles that use the roadway
system. The emissions from these sources are estimated by vehicle type and road type, and are
summed to the countywide level.
Biogenic sources are the natural sources like trees, crops, grasses and natural decay of plants.
The emissions from these sources are estimated at the grid cell level and summarized to the
county level.
For each type of emission inventory and each source classification, the pollutants inventoried
include VOC, NOx, PM2.5, coarse particulate (PM10), ammonia (NH3) and SO2. Table 3.5-1
presents a summary of the actual and typical 2002 annual emissions from the various source
sectors for the counties in the Hickory and Triad PM2.5 nonattainment areas. The full emission
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summaries for all counties in North Carolina and all states in the VISTAS/ASIP region can be
found in Appendix E.
Point Non-road
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Area Mobile
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Emissions reported as tons/year.
Point Non-road
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Area Mobile
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Emissions reported as tons/year.
Point Non-road
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Area Mobile
VOC NOx SO2 PM-10 PM-2.5 NH3 VOC NOx SO2 PM-10 PM-2.5 NH3
Actual
Typical
Emissions reported as tons/year.
In the sections that follow, a synopsis of the inventories used for each source classifications are
discussed. The detail discussions of the emissions inventory development can be found in
Appendix F. Further information on the emission inventory development for the entire southeast
and the inventories used for other RPOs can be found in Appendix P. Discussion of other input
requirements for SMOKE can also be found in Section 4.6 of the Modeling Protocol
(Appendix D.1).
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Point source emissions are emissions from individual sources having a fixed location. Generally,
these sources must have permits to operate, and their emissions are inventoried on a regular
schedule. Large sources having the potential to emit at least 100 tons per year (tpy) of a criteria
pollutant, 10 tpy of a single hazardous air pollutant (HAP), or 25 tpy total HAP are inventoried
annually. Smaller sources have been inventoried less frequently. The point source emissions
data can be grouped as EGU sources and other industrial point sources, also called non-electric
generating units (non-EGUs). Appendix F.1 documents the point source modeling inventory
development in more details
The actual base year inventory for the EGU sources used 2002 continuous emissions monitoring
(CEM) data reported to the USEPA’s Acid Rain program or 2002 hourly emissions data
provided by stakeholders. These data provide hourly emissions profiles for SO2 and NOx that
can be used in air quality modeling. Emissions profiles are used to estimate emissions of other
pollutants based on measured emissions of SO2 and NOx.
Emissions from EGU vary daily and seasonally as a function of variability in energy demand and
utilization and outage schedules. Since emission from EGUs represent a significant portion of
the emission inventory, a typical base year emissions inventory was developed to avoid
anomalies in future year emissions due to variability in meteorology, economic and outage
factors in 2002. This approach is consistent with the Attainment Modeling Guidance. To
develop a typical year 2002 emissions inventory for EGU sources, each unit’s average CEM heat
input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit specific
normalizing factor. This normalizing factor was then multiplied by the 2002 actual emissions.
The heat inputs for the period 2000 through 2004 were used because the modeling current design
values use monitored data from this same 5-year period. If a unit was shut down for an entire
year during the 2000 through 2004 period, the average of the years the unit was operational was
used. If a unit was shut down in 2002, but not permanently shutdown, the emissions and heat
inputs from 2001 (or 2000) were used in the normalizing calculations. For more information
about typical 2002 EGU emissions, please reference to Section 2.1.4 (EGU Analysis) of
Appendix F.1 (Point Source Emissions Inventory (EI) documentation).
As part of the air quality modeling, VISTAS, in cooperation with the other eastern RPOs,
contracted with ICF Resources, L.L.C., to generate future year emission inventories for the
electric generating sector of the contiguous United States using the Integrated Planning Model
(IPM). IPM is a dynamic linear optimization model that can be used to examine air pollution
control policies for various pollutants throughout the contiguous United States for the entire
electric power system. The dynamic nature of IPM enables projection of the behavior of the
power system over a specified future period. Optimization logic in IPM determines the least-cost
means of meeting electric generation and capacity requirements while complying with
specified constraints including air pollution regulations, transmission bottlenecks, and plant-specific
operational constraints. The versatility of IPM allows users to specify which constraints
to exercise, and to populate IPM with their own datasets. For more discussion on how the IPM
data was developed, please refer to Section 3.1.1 (Chronology of the Development of EGU
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Projections) and Section 3.1.2 (VISTAS/MRPO IPM runs for EGU sources) of Appendix F.1
(Point Source EI documentation).
The IPM modeling runs took into consideration both The Clean Air Interstate Rule (CAIR)
implementation and North Carolina’s Clean Smokestack Act (CSA) requirements for Duke
Power and Progress Energy. The VISTAS States and stakeholders also provided changes for the
following:
• NOx post-combustion control on existing units
• SO2 scrubbers on existing units
• SO2 emission limitations
• Particulate Matter (PM) controls on existing units
• Summer net dependable capacity
• Heat rate for existing units
• SO2 and NOx control plans based on State rules or enforcement settlements
For a detailed discussion about how IPM took consideration for federal, state and source-specific
requirements, please also refer to Appendix F.1.
For the non-EGU sources, the same inventory is used for both the actual and typical base year
emissions inventories. The non-EGU category uses annual emissions as reported under the
CERR for the year 2002. These emissions are temporally allocated to month, day, and hour
using source category code (SCC)-based allocation factors.
The general approach for assembling future year data was to use recently updated growth and
control data consistent with the USEPA’s CAIR analyses. This data was supplemented with
state-specific growth factors and stakeholder input on growth assumptions.
Stationary area sources are sources whose individual emissions are relatively small, but due to
the large number of these sources, the collective emissions could be significant (i.e., combustion
of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by
multiplying an emission factor by some known indicator of collective activity, such as fuel
usage, number of households, or population. Stationary area source emissions are estimated at
the countywide level.
A portion of the area source 2002 base year inventory for North Carolina was developed by the
NCDAQ and provided to the VISTAS/ASIP contractor. The VISTAS/ASIP contractor
calculated the remaining portion of the area source inventory. The sources estimated by the
contractor include emissions from animal husbandry, wild land fires, and particulate matter from
paved and unpaved roads. For the other states within the modeling domain, either state-supplied
data or data reported under CERR for 2002 was used.
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The actual base year inventory will serve as the typical base year inventory for all area source
categories except for wild land fires. For wild land fires, a typical year inventory was used to
avoid anomalies in wildfire activity in 2002 compared to longer-term averages. Development of
a typical year fire inventory provided the capability of using a comparable data set for both the
base year and future years. Thus, fire emissions remain the same for air quality modeling in both
the base and any future years. The VISTAS Fire Special Interest Work Group used State records
to ratio the number of acres burned over a longer term period (three or more years, as available
from state records) to 2002. Based on these ratios, the 2002 acreage was then scaled up or down
to develop a typical year inventory.
For categories other than wildland fires, the VISTAS/ASIP contractor generated the future base
year emissions inventory used in the attainment demonstration modeling. Growth factors
supplied from the states or the USEPA’s CAIR emission projections were applied to project the
controlled emissions to the appropriate year. In some cases, the USEPA’s Economic Growth and
Analysis System Version 5 growth factors were used if no growth factor was available from
either the states or the CAIR growth factor files. Appendix F.2 provides a detailed discussion of
the area source inventory.
Off-road (or non-road) mobile sources are equipment that can move but do not use the roadways,
such as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc.
For the majority of the non-road mobile sources, the emissions for 2002 were estimated using the
USEPA’s NONROAD2005c model. For the three source categories not included in the
NONROAD model, i.e., aircraft engines, railroad locomotives and commercial marine, more
traditional methods of estimating the emissions were used. The same inventory is used for both
the actual and typical base year emissions inventories.
For the source categories estimated using the USEPA’s NONROAD model, the model growth
assumptions were used to create the 2009 future year inventory. The NONROAD model takes
into consideration regulations affecting emissions from these source categories. For the four
largest airports in North Carolina, the Federal Aviation Administration’s Terminal Area Forecast
was used to project growth in aircraft emissions. For the commercial marine, railroad
locomotives and the remaining airport emissions, the VISTAS/ASIP contractor calculated the
future growth in emissions using detailed inventory data (both before and after controls) for 1996
and 2010, obtained from the CAIR Technical Support Document. When available, state-specific
growth factors were used. Appendix F.2 provides a detailed discussion of the non-road mobile
source inventory
For onroad vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. Key
inputs for MOBILE include information on the age of vehicles on the roads, the average speeds
on the roads, the mix of vehicles on the roads, any programs in place in an area to reduce
emissions for motor vehicles (such as emissions inspection programs), and temperature.
The MOBILE model takes into consideration regulations that affect emissions from this source
sector. The same MOBILE run is used to represent the actual and typical year emissions for
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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onroad vehicles using input data reflective of 2002. The MOBILE model is then run for the
2009 inventory using input data reflective of that year. The 2002 vehicle miles traveled (VMT),
speeds, vehicle age and vehicle mix data were obtained from the North Carolina Department of
Transportation (NCDOT). For urban areas in North Carolina that run travel demand models
(TDMs), the VMT and speed data from TDMs were used. For a detailed discussion about the
highway mobile source inventory development used in the attainment demonstration modeling,
please refer to Appendix F.3.
Biogenic emissions were prepared with the SMOKE-BEIS3 (Biogenic Emission Inventory
System 3 version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of the Urban Airshed
Model (UAM)-BEIS3 model. Modifications include use of MM5 data, gridded land use data,
and improved emissions characterization. The emission factors that are used in SMOKE-BEIS3
are the same as the emission factors as in UAM-BEIS3. The basis for the gridded land use data
used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Database
version 3 (BELD3) provided by the USEPA. A separate land classification scheme, based upon
satellite (AVHRR, 1 km spatial resolution) and census information aided in defining the forest,
agriculture, and urban portions of each county.
The base year biogenic emissions are used for the typical and future year modeling. This is a
common practice in air quality modeling since the same meteorology is used for all the modeling
years and the biogenic emissions are very dependent on the meteorology. Variation in these
emissions could impact the control strategies needed to demonstrate attainment. Therefore, these
emissions are kept constant.
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There are many aspects of model performance. This section will focus primarily on the methods
and techniques recommended by the USEPA for evaluating the performance of the air quality
model. Before the air quality model can be fully evaluated, an understanding of the
meteorological modeling performance is needed to understand potential biases and errors that
may be passed from the meteorological model directly into the air quality model. The
meteorological modeling evaluation is fully documented in Appendix I and is briefly
summarized in Section 4.1. The air quality modeling evaluation is fully documented in
Appendix J and is briefly summarized in Section 4.2.
Generally speaking, the meteorological modeling performance was quite good at both the 36-km
and 12-km grid resolutions. Synoptic features were routinely accurately predicted and the
meteorological model showed considerable skill in replicating the state variables (e.g.
temperature, mixing ratio, relative humidity, wind speed and direction, cloud cover, and
precipitation). The meteorological modeling performance statistics fell within expected and
acceptable ranges of error during the majority of the 2002 modeled year.
The meteorological modeling performance for North Carolina was very similar to the
performance for the VISTAS/ASIP region for the 12-km modeling domain. Again, large-scale
meteorological patterns were accurately predicted. The meteorological model demonstrated
substantial skill throughout the entire year and was especially skillful during the summertime
season from May through September.
For the North Carolina portion of the 12km modeling domain, the temperature bias was negative
for the entire year. The months of April through September had an average bias closer to zero
(- 0.1 Kelvin) than the fall and winter months. Overall, the diurnal pattern was captured very
well, with only a slight cool bias in the daytime, and a slight warm bias overnight.
Modeled mixing ratio followed observed trends fairly well. There was a slight low bias in the
morning through the early afternoon, and a high bias in the late afternoon and at overnight. The
bias values were generally near zero for most of the year (within ± 0.25 g/kg). Another
atmospheric moisture parameter, relative humidity, also showed a high bias in the daytime with a
low bias at night. Relative humidity biases tracked with temperature biases (higher in fall and
winter, lower in spring and summer), as it is a function of temperature. Precipitation has a
negative bias in the late fall (October through December) and a positive bias in the spring to
summer period. Though the model has a tendency to overestimate the amount of spring and
summertime precipitation, the spatial coverage of measurable precipitation is estimate fairly
well.
Wind speed had approximately 0.5 m/s (meters per second) high bias during the daytime hours,
and approximately 1 m/s high bias at overnight. This high bias is in part due to the inability of
the model to produce calm, or no wind condition. The models always have some level of winds
present. This is further aggravated by the fact that observation networks have a “starting
thresholds” for their wind speed instrumentation. The instruments need winds in excess of
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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1.34 m/s in order to register. As a result, wind speeds less than 1.34 m/s are reported as “calm”.
When omitting calm observations, the positive bias improves to between 0.2 to 0.6 m/s.
The meteorological model performance could have impact on the air quality model performance.
For example, the low temp bias in winter could impact the nitrate chemistry and allow for more
nitrate formation during this period. Moisture biases may impact secondary aerosol formation,
though it is questionable to what extent this may happen. Additionally, the under prediction of
precipitation in the late fall (October through December) may lead to over prediction of PM2.5.
Conversely the over prediction precipitation amounts in the April to September time frame may
lead to under prediction of total PM2.5 concentrations. Also, the slightly higher modeled wind
speeds could lead to additional dispersion of pollutants and ultimately to an under-prediction of
PM2.5 in the modeling results.
Overall, the NCDAQ believes that the meteorological model performance is adequate for this
modeling exercise and should produce credible inputs for the air quality modeling for the
attainment demonstration for the Hickory and Triad PM2.5 nonattainment areas.
Model performance analysis was completed with the final emissions inventory for the entire
VISTAS/ASIP 36km domain. For the full model performance evaluation for the 36-km domain,
please see the ASIP Technical support Document in Appendix P.
The remainder of the discussion of model performance presented here focuses on the comparison
of observational data from the FRM and STN monitoring sites and model output data from the
2002 actual annual air quality modeling. The evaluation primarily focuses on the air quality
model’s performance with respect to individual components of PM2.5, as good model
performance of the component species dictates good model performance of total or reconstituted
fine particulate matter. Model performance of the total fine particulate matter will also be
provided as a means to discuss the overall model performance for this Implementation Plan.
The air quality model evaluation focused on both the FRM and STN monitors across the state.
Designations were based on FRM monitors, and calculations of future design values are based on
current design value information from these sites. Since future attainment demonstrations hinge
on the model representing the FRM sites well, it follows that model performance for these sites
should be evaluated. STN data was also evaluated as this data is used to speciate the FRM data
so component based relative response factors can be calculated for each FRM monitoring site.
More detailed information on the attainment test process is described in Appendix L.
Only a brief summary of the model performance evaluation for the 12-km grid domain will be
discussed in the subsections to follow. For the full model performance evaluation for the 12-km
grid domain, please refer to Appendix J. A full model performance, including an analysis of
model statistics, scatter plots, time series, and stacked bar charts for the 12-km VISTAS/ASIP
domain, all North Carolina monitoring sites collectively, and individually for the monitoring
sites within the nonattainment area, please refer to Appendix J.
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In 2004, VISTAS/ASIP established model performance goals and criteria for components of fine
particle mass (Table 4.2.1-1) based on previous model performance for ozone and fine particles.
The Attainment Modeling Guidance for fine particulate matter at the time noted that PM models
might not be able to achieve the same level of performance as ozone models. VISTAS’s
evaluation considered several statistical performance measures and displays. Fractional bias and
mean fractional error were selected as the most appropriate metrics to summarize model
performance; other metrics were also calculated and are included for FRM and STN monitors in
the full model performance evaluation found in Appendix J.
<15 percent <35 percent Goal for PM2.5 model performance based on ozone
model performance, considered excellent performance
<30 percent <50 percent Goal for PM2.5 model performance, considered good
performance
<60 percent <75 percent Criteria for PM2.5 model performance, considered
average performance. Exceeding this level of
performance indicates fundamental concerns with the
modeling system and triggers diagnostic evaluation.
An additional way to evaluate model performance statistics is to visualize performance based on
these fractional bias and mean fractional error goals via “soccer plots” and “bugle plots”. The
soccer plot is so named because the dotted lines resemble a soccer goal. The soccer plot is useful
as both bias and error are shown on a single plot. As bias and error approach zero, the points are
plotted closer to or within the “goal”, represented here by the dashed boxes.
The bugle plot, named for the shape formed by the criteria and goal lines. The bugle plots are
shaped as such because the goal and criteria lines are adjusted based on the average
concentration of the observed species. As the average concentration becomes smaller, the
criteria and goal lines become larger to adjust for the model’s poor ability to predict at low
concentrations.
The analysis of bugle plots demonstrated that greater emphasis should be placed on performance
of those components with the greatest contribution to PM2.5 mass (e.g. SO4 and OC) and that
greater bias and error could be accepted for components with smaller contributions to total PM2.5
mass (e.g. EC, NO3, and soil). The soccer plots and bugle plots have been included as suggested
model performance evaluation displays in the Attainment Modeling Guidance.
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As a summary of model performance, soccer and bugle plots for the all of the VISTAS STN and
FRM monitors are included here. Plots have been developed for the average monthly modeled
concentrations and the performance statistics for all of the PM2.5 component species (SO4, NO3,
NH4, OC, and EC) and reconstructed PM2.5 total mass from the STN monitoring sites
(Figures 4.2.2-1 and 4.2.2-2), as well as the total PM2.5 mass from the FRM monitoring sites
(Figures 4.2.2-3 and 4.2.2-4).
The soccer plots for monthly average component performance for all the VISTAS/ASIP STN
sites shows generally good model performance for most species of PM2.5 and total PM2.5. The
exception is the prediction of NO3 values, which most values fall outside the criteria goal
(Figure 4.2.2-1). There are a few months that fall on the criteria level goal, which is better seen
in the zoomed view presented in the image on the right in Figure 4.2.2-1. However, when the
very low concentration of NO3 is taken into consideration, as presented in the bugle plots
(Figures 4.2.2-2), NO3 performance largely falls within the criteria and goal model performance
lines. One can still note a general tendency for under prediction in NO3, and other species in
right hand image in Figure 4.2.2-2, which leads to a slight under prediction in total reconstructed
PM2.5.
Monthly total PM2.5 concentration performance at all the VISTAS/ASIP FRM monitors largely
falls within goal level thresholds, with only three months falling just outside goal level
performance (Figure 4.2.2-3). Figure 4.2.2-4 suggests a slight negative bias in PM2.5 prediction
for most of the year, with mean fractional error values remaining within goal levels across the
year.
STN VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
STN VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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STN VISTAS Sites CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Bias
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
STN VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
FRM VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
FRM VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
North Carolina Attainment Demonstration August 21, 2009
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FRM VISTAS Sites CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Bias
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
FRM VISTAS Sites CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Error
PM2.5
Criteria
Goal
Overall, the general tendency is for the model to have some difficulty in predicting NO3, as the
monthly average values tend to fall outside the criteria goals for performance in the soccer plots.
Part of this under prediction lies in the fact that NO3 are generally found in low concentration
across the southeast, and the model generally has difficulties representing any compound with
low atmospheric concentrations. The bugle plots are more encouraging with NO3 performance,
as these plot take into consideration the concentration of the component when evaluating
performance. The bugle plots show all components and total PM2.5 falling within criteria level,
or better, of model performance goals. The weaker performance of NO3 accounts for the slight
negative bias in the both the total reconstructed PM2.5 mass from STN sites as well as FRM total
PM2.5 data.
The statistical metrics were calculated for the Hickory (Catawba County) and Hattie Avenue
(Forsyth County) STN monitors to demonstrate model performance for the components of PM2.5
in and near the PM2.5 nonattainment areas. Model performance statistics for the STN sites were
calculated on a component and total PM2.5 basis for the entire base year.
Model performance statistics were also calculated collectively for the FRM monitors within the
VISTAS 12-km domain, as well as individually for the 3 FRM monitors in the nonattainment
areas (Hickory, Lexington, and Mendenhall) to demonstrate the model’s ability to replicate total
PM2.5 mass at these sites. Summaries and statistical tables for the STN monitoring sites and
FRM monitoring sites can be found in Appendix J.
As a summary of model performance at the nonattainment area level, the soccer and bugle plots
for the Hickory STN (Figure 4.2.3-1 and 4.2.3-2) and FRM monitor (Figure 4.2.3-3 and 4.2.4-4)
follow. Plots have been developed for the average monthly concentrations of PM2.5 and its
component species at the STN sites, and for total PM2.5 from FRM monitors for all North
Carolina STN sites collectively and other the monitoring sites within the PM2.5 nonattainment
areas are included in Appendix J.
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Monthly average component concentration performance at the Hickory STN site is similar to
overall 12-km VISTAS domain and North Carolina statewide model performance. Nitrate
generally falls outside of suggested criteria model performance goals. Some under prediction of
organic carbon values is present, but this is in line with the overall model performance seen
across North Carolina. Overall, the PM2.5 model performance was within criteria level, if not
within the goal level thresholds.
STN Hickory CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
STN Hickory CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
STN Hickory CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Bias
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
STN Hickory CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 4.0 8.0 12.0 16.0 20.0
Average Concentration (μg/m3)
Mean Fractional Error
Sulfate
Nitrate
Ammon.
Organics
EC
PM2.5
Criteria
Goal
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FRM Hickory CMAQ 12km - 2002 Monthly
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
200.0
-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
FRM Hickory CMAQ 12km - 2002 Monthly
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
-75.0 -25.0 25.0 75.0
Fractional Bias
Mean Fractional Error
PM2.5
Criteria
Goal
FRM Hickory CMAQ 12km - 2002 Monthly
-200.0
-150.0
-100.0
-50.0
0.0
50.0
100.0
150.0
200.0
0.0 5.0 10.0 15.0 20.0 25.0
Average Concentration (μg/m3)
Mean Fractional Bias
PM2.5
(+) Criteria
(+) Goal
(-) Goal
(-) Criteria
FRM Hickory CMAQ 12km - 2002 Monthly
0.0
50.0
100.0
150.0
200.0
0.0 5.0 10.0 15.0 20.0 25.0
Average Concentration (μg/m3)
Mean Fractional Error
PM2.5
Criteria
Goal
Overall, the model performance for North Carolina through the 2002 baseline modeling year is
reasonable good. For the most part, mean normalized bias and mean normalized gross error are
within the recommended limits for good model performance for most of component species as
well as total PM2.5 mass. Overall performance was good for sulfate and organic carbon, which
are the largest constituents of PM2.5 for North Carolina. Nitrate performance was less than ideal,
especially during the summer months. This is likely due to the generally low atmospheric
concentrations seen in North Carolina. When the performance is weighted by the concentration,
as in the bugle plots, the performance metrics indicate better model performance. The model
also does a good job capturing PM2.5 component and total concentrations through various
The Hickory and Greensboro/Winston-Salem/High Point, NC PM2.5
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episode-clean out cycles (see Section 5, Appendix J). Overall, the NCDAQ believes that the
model performance is well within the limits of acceptable performance established in the
Attainment Modeling Guidance.
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Several control measures already in place or being implemented over the next few years will
reduce stationary point, highway mobile, and non-road mobile sources emissions. The Federal
and State control measures that have impacts on air quality in North Carolina were modeled for
the attainment year and are discussed in the sections below. Although all the control listed
below may not directly reduce PM2.5 concentrations in North Carolina, the modeling assessment
in this submittal was based on one atmosphere modeling completed for ozone and fine
particulate matter attainment demonstrations and regional haze plans.
Federal Tier 2 vehicle standards will require all passenger vehicles in a manufacturer’s fleet,
including light-duty trucks and Sport Utility Vehicles (SUVs), to meet an average standard of
0.07 grams of NOx per mile. Implementation began in 2004, with full compliance required 2007.
The Tier 2 standards will also cover passenger vehicles over 8,500 pounds gross vehicle weight
rating (the larger pickup trucks and SUVs), which are not covered by the current Tier 1
regulations. For these vehicles, the standards will be phased in beginning in 2008, with full
compliance required by 2009. The new standards require vehicles to be 77% to 95% cleaner
than those on the road today. The Tier 2 rule also reduced the sulfur content of gasoline to 30
parts per million (ppm) starting in January of 2006. Most gasoline sold in North Carolina prior
to January 2006 had a sulfur content of about 300 ppm. Sulfur occurs naturally in gasoline, and
interferes with the operation of catalytic converters on vehicles, which results in higher NOx
emissions. Lower-sulfur gasoline is necessary to achieve the Tier 2 vehicle emission standards.
New USEPA standards designed to reduce NOx and VOC emissions from heavy-duty gasoline
and diesel highway vehicles began to take effect in 2004. The second phase of the standards and
testing procedures, which began in 2007, will reduce particulate matter from heavy-duty
highway engines, and will also reduce highway diesel fuel sulfur content to 15 ppm since the
sulfur damages emission control devices. The total program is expected to achieve a 90%
reduction in PM emissions and a 95% reduction in NOx emissions for these new engines using
low sulfur diesel, compared to existing engines using higher-content sulfur diesel.
In May 2004, the USEPA promulgated new rules for large non-road diesel engines, such as those
used in construction, agricultural, and industrial equipment, to be phased in between 2008 and
2014. The non-road diesel rules also reduce the allowable sulfur in non-road diesel fuel by over
99%. Non-road diesel fuel currently averages about 3,400 ppm sulfur. The rule limits non-road
diesel sulfur content to 500 ppm by 2006 and 15 ppm by 2010. The combined engine and fuel
rules would reduce NOx and PM emissions from large non-road diesel engines by over 90%,
compared to current non-road engines using higher-content sulfur diesel.
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The new standard, effective in July 2003, regulates NOx, hydrocarbons (HC) and carbon
monoxide (CO) for groups of previously unregulated non-road engines. The new standard
applies to all new engines sold in the United States and imported after these standards begins and
applies to large spark-ignition engines (forklifts and airport ground service equipment),
recreational vehicles (off-highway motorcycles and all-terrain-vehicles), and recreational marine
diesel engines. The regulation varies based upon the type of engine or vehicle.
The large spark-ignition engines contribute to ozone formation and ambient CO and PM levels in
urban areas. Tier 1 of this standard was implemented in 2004 and Tier 2 started in 2007. Like
the large spark-ignition, recreational vehicles contribute to ozone formation and ambient CO and
PM levels. For the off-highway motorcycles and all-terrain-vehicles, the new exhaust emissions
standard was phased-in. Fifty percent of model year 2006 engines had to meet the standard, and
for model year 2007 and later, all of the engines have to meet the standard. Recreational marine
diesel engines over 37 kilowatts are used in yachts, cruisers, and other types of pleasure craft.
Recreational marine engines contribute to ozone formation and PM levels, especially in marinas.
Depending on the size of the engine, the standard began phasing-in in 2006.
When all of the non-road spark-ignition and recreational engine standards are fully implemented,
an overall 72% reduction in HC, 80% reduction in NOx, and 56% reduction in CO emissions are
expected by 2020. These controls will help reduce ambient concentrations of ozone, CO, and
fine PM.
In October 1998, the USEPA made a finding of significant contribution of NOx emissions from
certain states and published a rule that set ozone season NOx budgets for the purpose of reducing
regional transport of ozone (63 FR 57356). This rule, referred to as the NOx SIP Call, required
ozone season controls to be put on utility and industrial boilers, as well as internal combustion
engines, in 22 states in the Eastern United States. A NOx emissions budget was set for each state
and the states were required to develop rules that would assure that each state met its budget. A
NOx trading program was established, allowing sources to buy credits to meet their NOx budget
as opposed to actually installing controls. The emission budgets were to be met by the beginning
of 2004. Even with the trading program, the amount of ozone season NOx emissions has
decreased significantly in and around North Carolina.
On May 12, 2005, the USEPA promulgated the “Rule To Reduce Interstate Transport of Fine
Particulate Matter and Ozone (Clean Air Interstate Rule); Revisions to Acid Rain Program;
Revisions to the NOx SIP Call”, referred to as CAIR. This rule established the requirement for
States to adopt rules limiting the emissions of NOx and sulfur dioxide (SO2) and a model rule for
the states to use in developing their rules. The purpose of the CAIR is to reduce interstate
transport of precursors of fine particulate and ozone.
The CAIR applies to (1) any stationary, fossil-fuel-fired boiler or stationary, fossil-fuel-fired
combustion turbine serving at any time, since the start-up of a unit’s combustion chamber, a
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generator with nameplate capacity of more than 25 Megawatt hours (MW) producing electricity
for sale and (2) for a unit that qualifies as a cogeneration unit during the 12-month period starting
on the date that the unit first produces electricity and continues to qualify as a cogeneration unit,
a cogeneration unit serving at any time a generator with nameplate capacity of more than 25 MW
and supplying in any calendar year more than one-third of the unit’s potential electric output
capacity or 219,000 MW, whichever is greater, to any utility power distribution system for sale.
This rule provides annual state caps for NOx and SO2 in two phases, with the Phase I caps for
NOx and SO2 starting in 2009 and 2010, respectively. Phase II caps become effective in 2015.
The USEPA is allowing the caps to be met through a cap and trade program if a state chooses to
participate in the program. When fully implemented, the CAIR will reduce SO2 emissions in the
eastern United States by over 70 percent and NOx emissions by over 60 percent from 2003
levels. Due to Court challenges of CAIR in 2008, the USEPA will be making changes to this
program by 2011. However, the existing CAIR rules will remain in place until the USEPA
promulgates changes to the program.
North Carolina has adopted a number of regulations and legislation to address pollution issues
across the State. These include the Clean Air Bill, the NOx SIP Call Rule, the CSA, the Open
Burning Rule, and the CAIR. All of these regulations were modeled in the attainment
demonstration. These regulations are summarized below and the actual regulations and
legislation can be viewed in Appendix M.
The 1999 Clean Air Bill expanded the vehicle emissions inspection and maintenance program in
North Carolina from 9 counties to 48 counties between July 1, 2002 and January 1, 2006 (Figure
7.2.1-1). Vehicles are tested using the onboard diagnostic system (OBDII) test, an improved
method of testing for pollutant emissions.
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The effective dates for the counties in the Hickory and Triad PM2.5 nonattainment area are listed
in Table 5.2.1-1 below.
County Date
Catawba July 1, 2003
Davidson July 1, 2003
Guildford July 1, 2002
In response to the USEPA’s NOx SIP call, North Carolina adopted rules to control the emissions
of NOx from large stationary combustion sources. These rules cover (1) fossil fuel-fired
stationary boilers, combustion turbines, and combined cycle systems serving a generator with a
nameplate capacity greater than 25 MW and selling any amount of electricity, (2) fossil fuel-fired
stationary boilers, combustion turbines, and combined cycle systems having a maximum
design heat input greater than 250 million British thermal units per hour, and (3) reciprocating
stationary internal combustion engines rated at equal or greater than 2400 brake horsepower
(3000 brake horsepower for diesel engines and 4400 brake horsepower for dual fuel engines).
As part of the NOx SIP call, the USEPA rules established a NOx budget for sources in North
Carolina and other states.
Besides amending existing NOx rules and adopting new NOx rules specifically to address the
USEPA NOx SIP call, the North Carolina rules also require new sources to control emissions of
NOx. The objective of this requirement is (1) to aid in meeting the NOx budget for North
Carolina for minor sources and (2) to aid in attaining and maintaining the ambient air quality
standard for ozone in North Carolina.
North Carolina’s NOx SIP Call rule was predicted to reduce summertime NOx emissions from
power plants and other industries by 68% by 2006. In October 2000, the North Carolina
Environmental Management Commission (EMC) adopted rules requiring the reductions. In
2009, the North Carolina NOx SIP Call program was replaced with the North Carolina’s CAIR
rule, which is discussed below in Section 5.2.5.
In June 2002, the North Carolina General Assembly enacted the CSA, which requires coal-fired
power plants in North Carolina to reduce annual NOx emissions by 77% by 2009. These power
plants must also reduce annual sulfur dioxide emissions by 49% by 2009 and by 73% by 2013.
It is significant to note that this law sets a cap of NOx and SO2 emissions for the State, which the
public utilities cannot meet by purchasing emissions credits. The CSA reduces NOx emissions
beyond the requirements of the NOx SIP Call Rule. One of the first state laws of its kind in the
nation, this legislation provides a model for other states in controlling multiple air pollutants
from older coal-fired power plants.
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The rule adopted by the EMC in June 2004 is aimed at reducing emissions that contribute to
ozone and particle pollution when the air quality is expected to be poor. The ban is triggered on
"air quality action days," when the NCDAQ or local air programs forecast Code Orange, Red or
worse ozone conditions for a particular metro area. The following counties in the Hickory area
are subject to this rule Alexander Catawba Southeastern Burke and Southeastern Caldwell
counties. The following counties in the Triad area are subject to this rule Alamance Caswell
Davidson Davie Forsyth Guilford Randolph Rockingham and Stokes counties.
In response to the USEPA’s CAIR, the NCDAQ developed a state CAIR. Under the USEPA’s
rule, North Carolina has caps as follows:
• Annual NOx: 62,183 tons for 2009-2014 and
51,819 tons for 2015 and each year thereafter;
• Ozone season NOx: 28,392 tons for 2009-2014 and
23,660 tons for 2015 and each year thereafter;
• Annual SO2: 137,342 tons for 2010-2014 and
96,139 tons for 2015 and each year thereafter.
The State’s NOx allocations have been distributed among the covered facilities. The USEPA
will determine the SO2 allocations, which are based on the acid rain program. For the most part
the proposed rules incorporate the USEPA’s model rule. The USEPA’s model rule for
definitions; permitting; monitoring, reporting, and record keeping; trading and banking;
designated representative; opt-in provision, and new source growth are incorporated by
reference.
The rule requires the EMC to periodically review the allocations in 2010 and every five years
thereafter and to decide whether to reallocate. This rule does not preclude the EMC from
adopting additional emission reduction requirements for covered sources if necessary to attain or
maintain an ambient air quality standard.
The EMC adopted North Carolina’s CAIR on March 9, 2006 and the rule became effective
July 1, 2006. Due to the Court challenges of CAIR in 2008, the USEPA will be making changes
to this program soon. However, the existing CAIR rules will remain in place until the USEPA
promulgates changes to the program.
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An attainment demonstration consists of (a) analyses that estimate whether selected emissions
reductions will result in ambient concentrations that meet the NAAQS, and (b) an identified set
of control measures which will result in the required emissions reductions. The necessary
emission reductions for both of these attainment demonstration components may be determined
by relying on results obtained with air quality models.
Section 3.0 of the Attainment Modeling Guidance recommends applying both a modeled
attainment test and a subsequent screening test (or unmonitored area analysis) to the air quality
modeling results to determine if the annual PM2.5 NAAQS will be met. Additional technical or
corroboratory analyses may also be used as part of a “supplemental analysis” or a more stringent
“weight of evidence” determination to supplement the modeled attainment test and to further
support a demonstration of attainment of the annual PM2.5 NAAQS.
This section does not present a modeled attainment test or a subsequent screening test with
respect to the daily PM2.5 NAAQS, because all portions of North Carolina were initially
designated as attaining the daily PM2.5 standard. Continued attainment of the daily PM2.5
NAAQS is projected and assumed due to the widespread reductions in SO2 and NOx emissions
already discussed in Section 5 and the modeling projections discussed later in this Section that
demonstrate significant decreases in PM2.5 concentrations into the future.
The purpose of a modeling assessment is to determine if control strategies currently being
implemented (“on the books”) and proposed control strategies will lead to attainment of the
NAAQS for PM2.5 by the attainment year of 2009. The modeling is applied in a relative sense,
similar to the 8-hour ozone attainment test. However, the PM2.5 attainment test is more
complicated and reflects the fact that PM2.5 has many components. In the test, ambient PM2.5 is
divided into major components, with a separate relative response factor (RRF) and future design
value (DVF) calculated for each of the PM2.5 components. Since the attainment test is calculated
on a per species basis, the attainment test for PM2.5 is referred to as the Speciated Modeled
Attainment Test (SMAT). In its entirety, SMAT consists of four basic steps.
First, the observed quarterly mean PM2.5 and quarterly mean composition for each monitor is
calculated. This is achieved by multiplying the monitored quarterly mean concentration of PM2.5
from FRM monitors by the monitored fractional composition of PM2.5 species for each quarter
(e.g., (20% sulfate) x (15.0 μg/m3 PM2.5 mass) = 3.0 μg/m3 sulfate mass).
The monitored quarterly mean concentration of PM2.5 from FRM monitors are the 5 year
baseline design values (DVB) that are the result of averaging the 3 current design values (DVC)
that straddle the modeling base year. The fractional composition of PM2.5 species is derived
from STN monitoring site data that has been processed by the “sulfate, adjusted nitrate, derived
water, inferred carbonaceous material balance approach”, or SANDWICH method, so STN and
FRM masses are equivalent. The mean composition derived from the SANDWICH method
includes the percent of PM2.5 that can be attributed to SO4, NO3, OC, EC, other primary
inorganic particulates (or crustal materials), NH4, and particle bound water (PBW).
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The second step is to use model results to derive component specific RRF for each monitor for
each quarter. The RRF is basically the ratio of the model’s future projections to the baseline
current projections. For each component, the future year modeled quarterly mean concentration
predicted near the monitoring site divided by the base year modeled quarterly mean
concentration predicted near the monitoring site.
For the third step, the component specific RRFs are applied to the observed air quality
concentrations to project quarterly species estimates. For each quarter, the current quarterly
mean component concentration (step 1) are multiplied by the component-specific RRF obtained
in step 2. This leads to an estimated future quarterly mean concentration for each component.
The fourth step sums the quarterly components to get a quarterly mean PM2.5 value. These
quarterly mean values are then averaged to produce a future year annual average PM2.5 estimate,
or future design value (DVF), for each FRM monitoring site. This final value is then compared
to the NAAQS (15.0 μg/m3) to determine if attainment is reached. For a more detailed
discussion of SMAT and the data at each step for the monitors in the nonattainment areas, see
Appendix L.
The goal of the SMAT process is to sum the quarterly mean PM2.5 components to get annual
mean PM2.5 values. Table 6.2-1 displays the quarterly mean concentration and annual mean
future design values (DVFs) estimates for 2009 for the FRM sites in the North Carolina PM2.5
nonattainment areas.
These 2009 annual DVFs are the final product of the SMAT process and are then compared to
the NAAQS (15.0 μg/m3) to determine if attainment goals will be reached. Since the values at
the FRM site in both the nonattainment areas are less than 15.0 μg/m3, all areas have passed the
attainment test portion of the attainment demonstration.
The Attainment Modeling Guidance asserts that all attainment demonstrations should be
accompanied by supplemental analysis that further supports the modeling conclusions. This
supplemental analysis can include additional analyses of air quality, emissions and
meteorological data, and consider modeling outputs other than the results of the attainment test.
If the attainment test results fall short of the standard, the results of corroboratory analyses may
be used in a weight of evidence determination (WOE) to show that attainment is likely despite
modeled results, which may be inconclusive.
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The Attainment Modeling Guidance defines the guidelines for supplemental analysis/WOE for
the annual PM2.5 standard as follows:
- Site with a DVF less than 14.5 μg/m3 should submit basic supplemental analysis to
confirm the outcome of the model attainment test.
- Sites with a DVF between 14.5 and 15.5 μg/m3 should submit a weight of evidence
demonstration to aggregate supplemental analysis to support the model attainment
demonstration.
- Sites with a DVF greater than or equal to 15.5 μg/m3 should consider additional control
measure to ensure attainment, as more qualitative analysis is unlikely to attainment.
All North Carolina PM2.5 nonattainment areas have DVFs lower than 14.5μg/m3, making the
following section an examination of supplemental analysis to corroborate modeling results,
rather than a WOE analysis to show attainment.
Section 7.1 of the Attainment Modeling Guidance suggests several additional modeling exercises
that can be performed as part of supplemental analysis. One of the metrics that can be
considered as part of this type of additional analysis is the calculation of the percent change in
number of grid cells greater than or equal to 15 μg/m3 within the nonattainment area.
For the Hickory and Triad nonattainment areas, the cell counts of modeling data were tallied
from both the 2002 baseline and the 2009 attainment year modeling run for a subset of the
highest days from the base year. This was done in order to quantify the reduction of PM2.5 on
our highest days through out the year, and not just based on a single annual average from the
modeling. This subset of days included all days with a 24-hour PM2.5 concentration greater than
30 μg/m3 at any of the monitoring sites in either nonattainment area, as well as the four days with
the highest average daily values from each quarter. This selection process identified 28 days for
presentation and coincides with the days used in the model performance evaluation (Appendix J)
and in the model results section (Appendix K). A full listing of the days and the observed 24-
hour PM2.5 concentrations from the monitors in the nonattainment areas can be found in either
Appendix J or Appendix K.
Data was extracted for only the grid cells that contained portions of either of the PM2.5
nonattainment areas. Figure 6.3.1-1 highlights the 50 cells that encompass the North Carolina
PM2.5 nonattainment areas.
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The cell counts were binned based on concentration ranges of 15 μg/m3 intervals to help
illuminate the change in severity on the days in North Carolina with the highest PM2.5
concentrations. Figure 6.3.1-2 presents the cell count results both graphically and in tabular
form. The graph clearly shows a striking increase in the number of days below 15 μg/m3. By
2009, 41.57% of cells fall in the 0 –15 μg/m3 range, a substantial increase from the 17.21% in
2002. Raw cell counts show a total of 341 cells shifted to the 0 – 15 μg/m3 range between 2002
and 2009 (Table 6.3.1-1).
Figure 6.3.1-2 also shows a decrease in the number of cells in the 15 – 30 μg/m3 bin (269 cell
decrease) and the 30 - 45μg/m3 bin (75 cell decrease). The number of cells in the 45 –60 range
remain relatively constant from 2002 to 2009. A closer examination of the daily cell counts
shows that all of the cells in the highest concentration category occur on the same day in both the
2002 and 2009 modeling and are likely associated with a fire. Overall, the results from the air
quality modeling metric are encouraging. The metric shows a substantial increase in the number
of cells below 15 μg/m3, and an increase in cells below 30μg/m3.
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Modeling Year
Percentage of Nonattainment Area
341
-269
-75
3
One way to acquire modeling sensitivity runs is to examine the modeling results from other
RPOs or from USEPA modeling studies. Other modeling studies may use different physical and
chemical modeling options for their meteorological and air quality modeling runs, which would
provide a comparison or sensitivity based on these different options.
An air quality modeling exercise that contained results for North Carolina PM2.5 nonattainment
areas is the USEPA’s modeling for the CAIR. The Technical Support Document for the final
CAIR, March 2005, provided modeling results with and without the implementation for the
CAIR. Differences between the USEPA’s modeling and the attainment demonstration are: 1) the
meteorology was for 2001, 2) the DVB was the weighted design values for the 1999-2003 period
and 3) the modeling results were for 2010. The DVF was calculated using the CAIR SMAT
tool, so methodologies between the CAIR DVF and the values presented in Section 6.4 are the
same. These modeling results are listed in Table 6.3.2-1 below.
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The USEPA’s results were for the highest monitor in a county where more than one monitor is